Agents and multi-agent systems (MAS) have evolved from
autonomous problem-solving entities to integrating generative
AI and LLMs, ultimately leading to cooperative AI frameworks
that enhance adaptability, scalability, and collaboration. 
**Main Takeaways**
* Multi-agent systems have evolved from rule-based autonomy to cooperative AI,
emphasizing collaboration, negotiation, and ethical alignment.
* The rise of Agentic AI, driven by LLMs, introduces new opportunities for flexible
decision-making but raises challenges in efficiency and complexity.
* Integrating cooperative AI with generative models requires balancing
adaptability, transparency, and computational feasibility in multi-agent
environments 

**Context & History** 
The field of multi-agent systems emerged in the late 1980s/early 1990s,
with its main influences coming from two disparate areas [1,2]. One was the
field of AI robotics, which had begun to seriously address the issue of integrated agent architectures: how do we assemble several cognitive components of intelligence (planning, reasoning, learning, vision,…) into an integrated computational agent? The second was the nascent area of distributed AI, which studied how multiple AI systems could be made to solve problems cooperatively, by dynamically sharing information and tasks. By the mid1990s, these ideas had given rise to the new field, driven by the vision of having (semi)autonomous AI systems – agents – working on behalf of individual users in pursuit of their users’ goals, possibly interacting with other such agents in order to do so. A key insight
was that, since delegated goals might not necessarily be in harmony, it would be necessary to equip such agents with the ability to reason socially. Thus, while AI historically emphasised components of intelligence such as reasoning and problem-solving, the new field of multi-agent systems emphasised social skills such as cooperation, coordination, argumentation and negotiation. In order to underpin those skills, the development of models of Theory of Mind became central to the field. 
By the late 1990s, the field had its own conferences and journal, and was
firmly established as a key sub-field of AI. The area flourished from the late
1990s, with enormous energy devoted to (e.g.) communication languages
for autonomous agents, protocols for cooperation, coordination, and
negotiation, and the underpinning theory of these social skills. With
respect to the latter, while in the early years the practical reasoning paradigm of AI planning had been the dominant influence on the theory of multiagent systems, by the early part of this century, game theory had become the dominant theoretical foundation. Game theory, which emerged from the field of economics, is the theory of interaction between self-interested agents. Although originally devised as a tool for studying interactions between humans and human organisations, it nevertheless seemed a natural framework for studying interactions between artificial agents. A huge body of work emerged, studying (for example), how auctions might be used to allocate scarce resources, the theory
of negotiation between self-interested artificial agents, and how agents might optimally form teams to solve problems and share the associated benefits of cooperation. Interestingly, although learning in multi-agent systems was a key component of the field from the outset, it was not the centre of attention within the field in the first decade or so. 
This initial boom period for multiagent systems lasted roughly from
the mid 1990s to around 2010-15. By the end of that time, though,
some uncomfortable questions were beginning to be asked. While the field
had generated impressive quantities of scientific results, applications seemed to be thin on the ground. For sure there were some high-profile applications. The field of security games, which emerged from multi-agent systems, used ideas from game theory to allocate scarce security resources to defend high-profile targets such as airports. This work led to deployed
applications at US airports and ports. Automated high-frequence trading
systems, which plan and execute the bulk of trades on the world’s markets,
are multi-agent systems on a global scale. And agent-based modelling,
which models socio-technical systems at the level of individual decisionmakers, received a huge boost after the 2008 financial crisis, and again after the 2020 COVID-19 pandemic, where it was demonstrated to be an important tool for modelling the spread of contagion: financial in the first case; epidemiological and social policy in the second. But for all these successes, the core vision of multi-agent systems - where agents function in the context of other agents is an active area of research, exploring social concepts such as norms, organisations, practices and also values, is ongoing work, but for a large part outside the AAMAS community, but within social simulation research, and with results informing and
shaping policy-making in several areas from public health to transportation,
and urban transformation. 
While applications of multi-agent systems research (AI agents interacting
with other AI agents) has not, as yet, lived up to early expectations,
individual dialogue agents such as Alexa, Siri, Cortana are now an everyday
reality, and trace their historical roots both to work on intelligent agents
in the 1990s and work from the NLP community on dialog systems. Many
other applications of this thread of work have achieved success over the past three decades: automated call center assistants, customer service assistants, smartphone virtual assistants, smart speaker assistants, home robot assistants, that can converse with human users and accomplish tasks like ticket booking, restaurant reservations, online shopping, medical and health assistance, and sales assistance, empowered by AI modules like
speech recognition, natural language understanding, dialog management (i.e. state tracking and dialog policy), and natural language generation and speech synthesis. 
As ML surged in the early part of this century, activity in this area increased
within the multi-agent systems field. Multi-agent reinforcement
learning (MARL) grew to become the single biggest area within the field,
possibly driven in part by the fact that developing MARL experiments can be done relatively quickly and without recourse to expensive hardware. At the time of writing, while MARL represents a significant sub-field of ML as a whole, it seems to lack any clear unifying vision or direction – or application. 
**Current State & Trends** 
The emergence of LLMs from 2020 onwards has also led to increased
interest in agents [3]. LLMs can be used as part of a workflow to
automate routine tasks, and the general capabilities of such “agents” for
planning and problem solving is widely discussed. In this context, the concept of Agentic AI refers to the integration of generative AI and LLMs into autonomous agent frameworks aiming to leverage the generative capabilities of such models to enhance interaction, creativity, and real-time decision making in dynamic environments. As we write this (late 2024) there has been an explosion of startup companies hoping to commercialise such agents. Despite this renewed enthusiasm, the original aims of AAMAS from 30 years ago, such as building robust, autonomous multiagent systems capable of complex coordination and long-term reasoning, have not been fully realized. The extent to which this new wave of agent activity is informed by what went before is unclear. 
The challenge now is to understand what multi-agent systems mean in
the era of LLMs. The current direction of agentifying LLMs may lead to
overly complex and unnecessary architectures and heavy computational
costs, whereas adopting a multi-agent paradigm to the development and use of LLMs may offer a sustainable way to compose, diversify, and integrate approaches effectively. Even though distribution was one of the original drivers for the MAS field, this is still a largely unexplored direction under the current paradigm. Another trend nowadays is to recover ideas from classical cognitive architectures to add common sense skills to autonomous agents. 
An emerging trend is multi-agent architectures, which structure
AI components into modular systems that improve transparency,
adaptability, and ethical alignment. The focus on cooperative agents
highlights a shift toward AI that prioritizes collaboration, negotiation, and shared decision-making. By applying modularity, encapsulation,
and separation of concerns, these architectures enable scalable teamwork
between autonomous agents and humans, making them ideal for
hybrid AI applications requiring trust, explainability, and domain-specific
expertise. 
**Research Challenges** 
* Identify challenges and benefits of embedding GenAI-driven agents
into MAS, focusing on enhancing collaboration without disrupting
existing dynamics.
* Investigate how LLM-powered agents can improve negotiation and
decision-making in dynamic multiagent environments while ensuring
ethical alignment and safety.
* Develop architectures that integrate LLM-driven agents while maintaining
scalability, transparency, and computational efficiency in multiagent settings. 

1. Yoav Shoham. Agent-oriented programming. Artificial Intelligence. Artificial Intelligence. 60 (1): 51–92.
2. Michael Wooldridge. An Introduction to Multi-agent Systems 2e. Wiley, 2009.
3. Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic. Agents. Google whitepaper. https://archive.org/details/google-ai-agents-whitepaper

AI agents

The ability to reason has been a salient characteristic of human
intelligence, and there is a critical need for verifiable reasoning
in AI systems. 
**Main Takeaways** 
* Reasoning has always been seen as a core characteristic of human intelligence.
Reasoning is used to derive new information from given base knowledge; this
new information is guaranteed correct when sound formal reasoning is used,
otherwise it is merely plausible.
* AI research has led to a range of automated reasoning techniques. These
reasoning techniques have given rise to AI algorithms and systems, including
SAT, SMT, and constraints solvers as well as probabilistic graphical models, all of
which play a key role in critical real-world applications.
* While large pre-trained systems (such as LLMs) have made impressive
advancements in their reasoning capabilities, more research is needed to
guarantee correctness and depth of the reasoning performed by them; such
guarantees are particularly important for autonomously operating AI agents 

**Context & History** 
Reasoning is a core component of
human intelligence. From the dawn
of humanity, abductive reasoning
has been used to predict danger and
inductive reasoning made it possible
to learn regularities governing the
world. Beginning in Ancient Greece,
deductive reasoning techniques were
developed to draw valid conclusions
that follow logically from premises
known to be true. The development
of reasoning methods with such a
priori guarantees was a key factor in
the advancement of modern science,
mathematics, and engineering; notably,
according to philosophers such as
Charles Sanders Peirce, the interplay
between abduction, deduction, and
induction forms the basis of the
scientific method and hence all modern
science. Attempts to mechanize
logical reasoning can be traced back to
13th-century philosopher Ramon Lull
and lie at the heart of the concept of
computation. Probabilistic reasoning
and inference have also profoundly
impacted reasoning, often relying on
the celebrated theorem by Thomas
Bayes on inverse probability that also
forms the basis for many machine
learning and statistics approaches.
Finally, the evaluation of correct (sound)
reasoning lies at the heart of most
quantitative assessments of human
cognition. 
Not surprisingly, reasoning has been
central to the AI enterprise. Indeed,
the earliest research in AI – from Logic
Theorist onwards [1] – had a strong
focus on reasoning [2]. Since the 1960s,
AI has also embraced probabilistic
reasoning and models, initially for
medical diagnosis [3]. Since then,
the reasoning tasks addressed in AI 
research have covered the gamut from
planning and temporal reasoning to
diagnosis and explanation. While early
AI has paid attention to both plausible
reasoning (case-based, analogical,
qualitative) and sound formal reasoning
with guarantees (logical, probabilistic,
constraint-based), over the years,
the focus has shifted more towards
reasoning with formal guarantees.
There are good reasons for this when
designing AI systems and techniques
that compensate for human limitations
and weaknesses since reasoning
with guarantees is challenging for
humans. This has led to practically
impactful applications of AI systems
such as SAT, SMT, and constraints
solvers, including the verification of
correctness properties of computer
hardware and software, the safety of
communications protocols, the design
of new proteins, and, more recently, the
robustness of neural networks against
adversarial attacks. It has also resulted
in probabilistic graphical models [4,
5], which are powerful modeling and
inference tools that have found their
way into numerous applications of
reasoning in medicine, robotics, and
beyond. 
**Current State & Trends** 
The emergence of the Internet and the
associated technology that made it
possible to capture the human digital
footprint at scale, as well as the leaps in
computing power, have made possible
novel approaches to learning bottomup from data. Of particular interest are
large pre-trained models, such as LLMs,
that have shown surprising abilities in
plausible reasoning. Unlike the earlier
research on reasoning in AI, LLMs
have focused on plausible reasoning patterns as they emerge automatically
after large-scale training on petabyte
corpora. While the results have been
quite remarkable so far, the reasoning in
this context has been of the “plausible”
variety with no guarantees. 
Meanwhile, sound formal reasoning
techniques remain key to important
and impactful applications of cuttingedge AI technology for the verification
of computer hardware and software,
as well as for real-world planning and
resource allocation problems. They
are also increasingly recognized as a
crucial basis for the formal verification
of machine learning techniques such
as neural networks, e.g., in the context
of local robustness against adversarial
attacks [6]. Significant research activity
takes place in these areas, focusing on
improving various types of reasoning
algorithms (notably with respect to their
computational complexity), leveraging
learning within sound formal reasoning,
and combining reasoning and learning
techniques [7, 8]. 
**Research Challenges** 
Bringing some of the rigorous a priori or
post hoc guarantees back into plausible
reasoning patterns turbocharged by
the pre-trained models has become an
active and promising area of research
– especially where AI systems need to
work autonomously in safety-critical
domains. Research on so-called “large
reasoning models” as well as on neurosymbolic approaches is addressing
these challenges. 
Furthermore, even though formal
reasoning with correctness guarantees
is currently considerably less in vogue
than the use of generative AI techniques
for plausible reasoning, formidable and essential challenges also remain in that
area. In this context, the combination
of machine learning techniques with
formal reasoning techniques holds
considerable promise for economically
and socially valuable breakthroughs,
notably in the area of AI safety and
transparency.
The questions and challenges we face
range from the philosophical:
* What exactly is “reasoning”?

to the practical:
* Can LLM ‘reasoning’ be trusted?

and include: 
What does the future hold for the
advancement and role of symbolic
reasoning?
* To what extent can LLMs or other
generative models reproduce or
replace symbolic reasoning?
* To what degree will symbolic
reasoning be necessary or sufficient
to overcome the current limitations
of LLMs?
* How well can AI reasoning,
especially LLM ‘reasoning,’ be
explained and understood?
* How can computers better
understand and simulate human
reasoning?
* What is the role of collaborative
reasoning between humans and
computers?
* How best can LLMs and symbolic
reasoning be integrated into “neurosymbolic reasoning”?
* Are further breakthroughs, beyond
both LLMs and traditional symbolic
reasoning, required to achieve AGIlevel reasoning?
* What forms of reasoning can best
support humans when dealing with
various challenges, e.g., in medical,
scientific, engineering, and legal
domains? 

**Community Opinion** 
The AAAI community appears to
strongly agree on the importance
of reasoning in AI systems. In our
community survey, slightly over 55%
of the respondents chose to answer
specific questions related to the topic
of reasoning. Of these, 79% indicated
that the topic of reasoning is relevant
to their research (with 44.7% marking
it as “very relevant”). Of the properties
required for referring to a process
as reasoning, 77.5% of the survey
participants marked “Knowledge can
be incorporated”, 72.5% “Explanations
can be provided,” and 56.9% “Involves
multiple steps to arrive at a conclusion”.
Interestingly, merely 37.4% indicated
“Guaranteed correctness of inference
results/outcomes”, and only 23.7% that
“A formal system and solver is used,”
which reflects the recent focus on
informal, plausible reasoning, likely in
the context of generative AI methods.
This suggests that an effort may be
warranted to better communicate the
importance and success of formal,
sound reasoning techniques. Finally,
44.7% of respondents agreed that
“Reasoning involves a search process.” 
There was broad agreement among
survey participants that focusing
reasoning research in AI on humanlevel reasoning is valuable (41.6%) or
even essential (47%); similarly, a focus
on domain-specific reasoning abilities
was seen by 49.6% of respondents as
valuable, and by 42.8% as essential.
This clearly reflects the importance
attributed to a research focus on
reasoning. 
The community also sees an exciting
potential of synergy offered by logical
and probabilistic models of reasoning
that were developed in AI prior to large
pre-trained models. This is clearly
reflected in the fact that 76.9% of survey
participants marked the integration
of learning and reasoning approaches
as very important (6 or 7 on a scale
of 7); interestingly, the percentage
of respondents that considered
Explainability and verifiability as very
important was similarly high (at 71.7%). 
Finally, 61.8% of survey participants
estimated the minimal percentage of
symbolic AI techniques required for
reaching human-level reasoning to be
at least 50% (with 24.8% estimating it
at 75% or more, compared to 38.2%
estimating it at 25% or below). What
remains unclear is the degree to
which AI researchers and practitioners
realize that decidedly superhuman
levels of reasoning are required for
and displayed in the prominent and
successful applications of formal AI
reasoning techniques for scientific
and mathematical discovery and
engineering applications, as well as in AI
safety. 
1. Newell, A. & Simon, H. (1956). The logic theory machine: A complex information processing system. IRE Transactions on Information Theory 2: 61-79. 
2. Brachman, R. and Levesque, H. (2004) Knowledge Representation and Reasoning (1st Ed). Morgan Kaufman. 
3. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach (4th Ed). Pearson. 
4. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman. 
5. Koller, D. and Friedmann, N. (2009) Probabilistic Graphical Models. The MIT Press. 
6. König, M. et al. (2024) Critically Assessing the State of the Art in Neural Network Verification. Journal of Machine Learning Research 25(12): 1-53 
7. Guo, D. et. al. (2025) DeepSeek-R: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. https://arxiv.org/abs/2501.12948 
8. Kambhampati, S. (2024). Can Large Language Models Reason and Plan? Annals of New York Academy of Sciences. March 2024.

AI Reasoning

AI & Sustainability

Artificial General Intelligence (AGI)

How should we challenge exaggerated claims about AI’s
capabilities and set realistic expectations? 
**Main Takeaways** 
* Over the last 70 years, against a background of constant delivery of new and important technologies, many AI innovations have generated excessive hype.
* Like other technologies these hype trends have followed the general Gartner Hype Cycle characterization.
* The current Generative AI Hype Cycle is the first introduction to AI for perhaps the majority of people in the world and they do not have the tools to gauge the validity of many claims 

**Context & History** 
Artificial intelligence, or AI, is the field
that studies the synthesis and analysis
of computational agents that act
intelligently [6]. AI has gone through
hype cycles multiple times since the
1956 workshop that established the
name AI and set the course for early
computer science departments to
include AI as a major component of
their research and teaching. All hype
bubbles eventually burst, as the
essence of hype is that it is beyond
reality. Over the decades this has led to
AI winters where funding has dried up
for all of AI or for specific aspects of AI
such as neural networks or robotics. 
A study published 2017 on trends with
the public perception of AI over a 30-
year period found that discussion about
AI had sharply increased since 2009
and that discussions in the public press
had been consistently more optimistic
than pessimistic [4]. The study also
found that hopes about AI applications
of AI in healthcare and education
were increasing over time. Another
finding was that concerns were growing
about loss of control of AI, ethical
implications, and the negative impact of
AI on work. 
Perhaps the difference in recent years
with prior periods is that hype has
gone beyond the pages of academic
conferences, conference papers, and
scientific magazines, out into both the
mainstream media, and social media.
AI and Artificial Intelligence have
become common words that nontechnical people have heard about
and a common subject for leaders
of almost all countries to talk about.
Governments, for the first time, have AI
policies. 
One of the problems is that AI is
actually a wide-reaching term that can
be used in many different ways. But
now in common parlance it is used
as if it refers to a single thing. In their
2024 book [5] Narayanan and Kapoor
likened it to the language of transport
having only one noun, ‘vehicle’, say, to
refer to bicycles, skate boards, nuclear
submarines, rockets, automobiles, 18
wheeled trucks, container ships, etc.
It is impossible to say almost anything
about ‘vehicles’ and their capabilities
in those circumstances, as anything
one says will be true for only a small
fraction of all ‘vehicles’. This lack of
distinction compounds the problem
of hype, as particular statements get
overgeneralized. 
The hype also sets expectations for
ordinary people. Many are fearful that
they will lose their jobs to AI in the
short term. Social scientists then work
to solve labor disruptions, e.g., for
displaced truck drivers [6], based on
predictions about AI (and in this case,
self-driving trucks) and its adoption that
turn out to be wildly optimistic. There
are no deployed self-driving trucks in
the predicted time frame. 
Hype in response to a technology
trigger is not restricted to AI. Indeed
the business intelligence company
Gartner, has deliberately made
a practice of using a graphical
representation of hype levels through
five stages that are common for many
technologies: (1) technology trigger,
(2) peak of inflated expectations, (3)
trough of disillusionment, (4) slope
of enlightenment, and (5) plateau
of productivity. They have used this
framework to track many technologies,
including quantum computers, block
chain, autonomous vehicles, nanotechnology, etc. In November 2024 they
[1] estimated that hype for Generative
AI had just passed its peak and was on
the downswing. 
The question for AI professionals is
how to respond to this hype, how to
question it, and how to help others
understand what is hubris, while
maintaining their own intellectual
modesty and probity. This is hard to do
in the middle of outsized claims about
one’s own field, and often it is up to
future historians to carefully dissect
past scientific arguments. 
Historian Thomas Haigh has tried to
do such a dissection, almost in real
time, in a recent series of articles in the
Communications of the ACM. In [2] he
gives a post-mortem on the impact of
over-hype in AI that resulted in what is
known as the AI-winter in the 1980s. His
one line summary is: “Fallout from an
exploding bubble of hype triggered the
real AI Winter in the late 1980s.” In [3] he
makes a comparison between the hype
of today and of those earlier times.
He summarizes this particular opinion
piece with the line: “From engines of
logic to engines of bullshit?” 
**Research Challenges** 
Many of us who have worked in AI for
decades face the challenge of trying to
remain honest brokers when we see
that many of the public statements of
people quite new to the field are out of
line with reality. 
The big question is whether, given
the dynamics of social media and the
search for clicks, professional opinions
and peer reviewed research papers
have any impact on dampening the
overclaims and the ways they distort
common understanding of where AI is,
and what is its potential in one year, five
years, ten years, etc. 
If we are currently left out of the
conversations how can we change that? 
**Community Opinion** 
The Community Survey gives
perspectives on the reactions to the AI
Perception vs Reality theme. First, the
results of the survey are summarized
here. 36% of the survey respondents
chose to answer the questions for this
theme. This is the summary breakdown
of the responses to each question: 
**1. How relevant is this Theme for your
own research?** 72% of respondents
said it was somewhat relevant (24%),
relevant (29%) or very relevant (19%). 
**2. The current perception of AI
capabilities matches the reality of AI
research and development.** 79% of
respondents disagreed (47%) or strongly
disagreed (32%). 
**3. In what way is the mismatch
hindering AI research?** 90% of
respondents agreed that it is hindering
research: 74% agreeing that the
directions of AI research are driven by
the hype, 12% saying that theoretical AI
research is suffering as a result, and 4%
saying that less students are interested
in academic research. 
**4. Should there be a communitydriven initiative to counter the hype
by fact-checking claims about AI?** 78%
yes; 51% agree and 27% strongly agree. 
**5. Should there be a communitydriven initiative to organize public
debates on AI perception vs reality,
with video recordings to be made
available to all?** 74% yes; 46% agree
and 28% strongly agree. 
**6. Should there be a communitydriven initiative to build and maintain
a repository of predictions about
future AI’s capabilities, to be checked
regularly for validating their accuracy?**
59% yes; 40% agree and 29% strongly
agree. 
**7. Should there be a communitydriven initiative to educate the public
(including the press and the VCs) about
the diversity of AI techniques and
research areas?** 87% yes; 45% agree
and 42% strongly agree. 
**8. Should there be a communitydriven initiative to develop a method
to produce an annual rating of the
maturity of the AI technology for
several tasks?** 61% yes; 42% agree and
19% strongly agree. 
Since the respondents to this theme
are self-selected (about a third of all
respondents), that bias must be kept
in mind. Of those who responded,
a strong and consistent (though not
completely monolithic) portion felt that
the current perception of AI capabilities
was overblown, that it had a real impact
on the field, and that the field should
find a way to educate people about the
realities.

1. Chandrasekaran, A. [2024]. What’s Driving the Hype Cycle for Generative AI, 2024. https://www.gartner.com/en/articles/hype-cycle-for-genai
2. Haigh, T. [2024]. How the AI Boom Went Bust. Vol. 67 No. 2, pp 22-26.
3. Haigh, T. [2025]. Artificial Intelligence Then and Now. Vol. 68 No. 2, pp 24-29.
4. Fast, E. and Horvitz, E. [2017]. Long-term trends in the public perception of artificial intelligence, AAAI’17: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence,
February 4, 2017, pp 963-969. https://ojs.aaai.org/index.php/AAAI/article/view/10635
5. Narayanan, A. and Kapoor, S. [2024]. AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference. Princeton University Press.
6. Poole, D. L. and Mackworth, A. K. [2023]. Artificial Intelligence: Foundations of Computational Agents, 3rd edition. Cambridge University Press.
7. Wang, S., Mack, E. A., Van Fossen, J. A., Medwid, M., Cotten, S. R., Chang, C. H., Mann, J., Miller, S. R., Savolainen, P.T. and Baker, N. [2023]. Assessing alternative occupations for truck
drivers in an emerging era of autonomous vehicles. Transportation Research Interdisciplinary Perspectives, Vol 19. May. https://doi.org/10.1016/j.trip.2023.100793

AI Perception vs. Reality

Towards mechanistic explanation of human language processing

Large Language Models have demonstrated impressive reasoning capabilities across multiple languages. However, the relationship between capabilities in different languages is less explored. In this work, we decompose the process of reasoning tasks into two separated components: knowledge retrieval and knowledge-free reasoning, and analyze the relationship between cross-lingual transferability and these two components. With adapted commonsense reasoning datasets and constructed knowledge-free reasoning datasets, we show that the knowledge-free reasoning capability can be nearly perfectly transferred across various source-target language directions despite the secondary impact of resource in some specific target languages, while cross-lingual knowledge retrieval significantly hinders the transfer. Moreover, by analyzing the hidden states and feed-forward network neuron activation during the reasoning, we show that higher similarity of hidden representations and larger overlap of activated neurons could explain the better cross-lingual transferability of knowledge-free reasoning than knowledge retrieval. Thus, we hypothesize that knowledge-free reasoning shares similar neurons in different languages for reasoning, while knowledge is stored separately in different languages.

Large Language Models Are Cross-Lingual Knowledge-Free Reasoners

Piaget's Theory of Cognitive Development (PTC) posits that the development of cognitive levels forms the foundation for human learning across various abilities. As Large Language Models (LLMs) have recently shown remarkable abilities across a wide variety of tasks, we are curious about the cognitive levels of current LLMs: to what extent they have developed and how this development has been achieved. To this end, we construct a benchmark CogLM (Cognitive Ability Evaluation for Language Model) based on PTC to assess the cognitive levels of LLMs. CogLM comprises 1,220 questions spanning 10 cognitive abilities crafted by more than 20 human experts, providing a comprehensive testbed for the cognitive levels of LLMs. Through extensive experiments across multiple mainstream LLMs with CogLM, we find that: (1) In our testing framework, advanced LLMs (such as GPT-4) have demonstrated human-like cognitive abilities, comparable to those of a 20-year-old human. (2) The parameter size and optimization objective are two key factors affecting the cognitive levels of LLMs. (3) The performance on downstream tasks is positively correlated with the level of cognitive abilities. These findings fill the gap in research on the cognitive abilities of LLMs, tracing the development of LLMs from a cognitive perspective and guiding the future direction of their evolution.

CogLM: Tracking Cognitive Development of Large Language Models

Long-term memory is important for chatbots and dialogue systems (DS) to create consistent and human-like conversations, evidenced by numerous developed memory-augmented DS (MADS). To evaluate the effectiveness of such MADS, existing commonly used evaluation metrics, like retrieval accuracy and perplexity (PPL), mainly focus on query-oriented factualness and language quality assessment. However, these metrics often lack practical value. Moreover, the evaluation dimensions are insufficient for human-like assessment in DS. Regarding memory-recalling paradigms, current evaluation schemes only consider passive memory retrieval while ignoring diverse memory recall with rich triggering factors, e.g., emotions and surroundings, which can be essential in emotional support scenarios. To bridge the gap, we construct a novel Memory-Augmented Dialogue Benchmark (MADail-Bench) covering various memory-recalling paradigms based on cognitive science and psychology theories. The benchmark assesses two tasks separately: memory retrieval and memory recognition with the incorporation of both passive and proactive memory recall data. We introduce new scoring criteria to the evaluation, including memory injection, emotion support (ES) proficiency, and intimacy, to comprehensively assess generated responses. Results from cutting-edge embedding models and large language models on this benchmark indicate the potential for further advancement. Extensive testing further reveals correlations between memory injection, ES proficiency, and intimacy.

MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generation

The scarcity of domain-specific dialogue datasets limits the development of dialogue systems across applications. Existing research is constrained by general or niche datasets that lack sufficient scale for training dialogue systems. To address this gap, we introduce $\textbf{DiaSynth}$ - a synthetic dialogue generation framework capable of generating high-quality, contextually rich dialogues across a wide range of domains. Unlike existing frameworks, DiaSynth uses Large Language Models (LLMs) and Chain of Thought (CoT) reasoning to generate dynamic, domain-specific dialogues with simulated personas and diverse conversational features. We perform our experiments by generating synthetic data using different LLMs and few-shot examples from DialogSum and SAMSum. The pretrained language models fine-tuned on the synthetic data outperform the base models by $\textbf{16.47\%}$ on dialogue summarization, while the comparison between models fine-tuned on in-domain data and synthetic data shows that the synthetic data is able to capture $\textbf{90.48\%}$ of the performance distribution of the in-domain data on dialogue summarization. The quality of the data generated also increases as we increase the size of LLM from 3B to 8B. These results validate DiaSynth's potential as a robust alternative to traditional data collection methods. We open source the code and data generated for future research.

DiaSynth: Synthetic Dialogue Generation Framework for Low Resource Dialogue Applications

Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. To facilitate the training for these agents with both linguistic feedback and non-linguistic reward signals, we introduce Learning through Communication (LTC). We design a universal buffer to store all the feedback, and an iterative pipeline to enable an LLM agent to explore and update its policy in an given environment. To optimize agent interactions for task-specific learning with our universal buffer and pipeline, we introduce diverse communication patterns tailored for both single-agent and multi-agent environments. We evaluate the efficacy of our LTC approach on four diverse datasets: ALFWorld (single-agent), HotpotQA (multi-agent collaboration), Chameleon (multi-agent competition), and GSM8k (multi-agent teacher-student). On these data sets, LTC outperforms the supervised instruction fine-tuning baselines by 3.6% to 12%. These results highlight the versatility and efficiency of LTC in facilitating online adaptation for LLM agents.

Adapting LLM Agents with Universal Communication Feedback

Multi-agent systems, which consist of multiple AI models interacting within a shared environment, are increasingly used for persona-based interactions. However, if not carefully designed, these systems can reinforce implicit biases in large language models (LLMs), raising concerns about fairness and equitable representation. We present MALIBU, a novel benchmark developed to assess the degree to which LLM-based multi-agent systems implicitly reinforce social biases and stereotypes. MALIBU evaluates bias in LLM-based multi-agent systems through scenario-based assessments. AI models complete tasks within predefined contexts, and their responses undergo evaluation by an LLM-based multi-agent judging system in two phases. In the first phase, judges score responses labeled with specific demographic personas (e.g., gender, race, religion) across four metrics. In the second phase, judges compare paired responses assigned to different personas, scoring them and selecting the superior response. Our study quantifies biases in LLM-generated outputs, revealing that bias mitigation may favor marginalized personas over true neutrality, emphasizing the need for nuanced detection, balanced fairness strategies, and transparent evaluation benchmarks in multi-agent systems.

MALIBU Benchmark: Multi-Agent LLM Implicit Bias Uncovered

Transformer architectures have become the backbone of domains such as language modeling but often suffer in many inference settings due to their quadratic self-attention. Recently proposed subquadratic architectures, such as State Space Models (SSMs), reveal promising results ranging from more efficient compute to outperforming transformer-based models on certain benchmarks. Theory of Mind (ToM)--the ability to reason about one’s own and other’s mental states--is a complex task that poses significant challenges to state-of-the-art models. While previous works have focused on ToM reasoning solely in transformer-based models, we evaluate a leading transformer-based model and a leading SSM on various long context ToM tasks. Through our evaluation, we reveal that although the SSM generally performs worse on ToM tasks compared to the transformer-based model, the SSM exhibits less performance degradation on more difficult tasks, indicating potential advantages for SSMs in difficult and long-context ToM reasoning.

DIG-DIS: Transformer-Based Models vs State Space Models on Complex Theory of Mind Reasoning Tasks

Retrieval augmented generation has revolutionized large language model (LLM) outputs by providing factual supports. Nevertheless, it struggles to capture all the necessary knowledge for complex reasoning questions. Existing retrieval methods typically divide reference documents into passages, treating them in isolation. These passages, however, are often interrelated, such as passages that are contiguous or share the same keywords. Therefore, it is crucial to recognize such relatedness for enhancing the retrieval process. In this paper, we propose a novel retrieval method, called GNN-Ret, which leverages graph neural networks (GNNs) to enhance retrieval by exploiting the relatedness between passages. Specifically, we first construct a graph of passages by connecting passages that are structure-related or keyword-related. A graph neural network (GNN) is then leveraged to exploit the relationships between passages and improve the retrieval of supporting passages. Furthermore, we extend our method to handle multi-hop reasoning questions using a recurrent graph neural network (RGNN), named RGNN-Ret. At each step, RGNN-Ret integrates the graphs of passages from previous steps, thereby enhancing the retrieval of supporting passages. Extensive experiments on benchmark datasets demonstrate that GNN-Ret achieves higher accuracy for question answering with a single query of LLMs than strong baselines that require multiple queries, and RGNN-Ret further improves accuracy and achieves state-of-the-art performance, with up to 10.4 accuracy improvement on the 2WikiMQA dataset.

Graph Neural Network Enhanced Retrieval for Question Answering of Large Language Models

Large Language Models with chain-of-thought prompting, such as OpenAI-o1, have shown impressive capabilities in natural language inference tasks. However, Multi-hop Question Answering (MHQA) remains challenging for many existing models due to issues like hallucination, error propagation, and limited context length. To address these challenges and enhance LLMs' performance on MHQA, we propose the Self-Guiding prompting Finite State Machine (SG-FSM), designed to strengthen multi-hop reasoning abilities. Unlike traditional chain-of-thought methods, SG-FSM tackles MHQA by iteratively breaking down complex questions into sub-questions, correcting itself to improve accuracy. It processes one sub-question at a time, dynamically deciding the next step based on the current context and results, functioning much like an automaton. Experiments across various benchmarks demonstrate the effectiveness of our approach, outperforming strong baselines on challenging datasets such as Musique. SG-FSM reduces hallucination, enabling recovery of the correct final answer despite intermediate errors. It also improves adherence to specified output formats, simplifying evaluation significantly.

SG-FSM: A Self-Guiding Zero-Shot Prompting Paradigm for Multi-Hop Question Answering Based on Finite State Machine

Existing multimodal summarization approaches struggle with scenarios involving numerous images as input, leading to a heavy load for readers. Summarizing both the input text and numerous images helps readers quickly grasp the key points of multimodal input. This paper introduces a novel task, Numerous Images-Oriented Multimodal Summarization (NIMMS). To benchmark this task, we first construct the dataset based on a public multimodal summarization dataset. Considering that most existing metrics evaluate summaries from a unimodal perspective, we propose a new Multimodal Information evaluation (M-info) method, measuring the differences between the generated summary and the multimodal input. Finally, we compare various summarization methods on NIMMS and analyze associated challenges. Experimental results have shown that M-info correlates more closely with human judgments than five widely used metrics. Meanwhile, existing models struggle with summarizing numerous images. We hope that this research will shed light on the development of multimodal summarization. Furthermore, our code and dataset will be released to the public.

Pay More Attention to Images: Numerous Images-Oriented Multimodal Summarization

Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data, thereby introducing new dimensions of potential attacks and complex risk combinations. In this paper, we begin with a detailed analysis aimed at disentangling risks through step-by-step reasoning within multimodal inputs. We find that systematic multimodal risk disentanglement substantially enhances the risk awareness of MLLMs. Via leveraging the strong discriminative abilities of multimodal risk disentanglement, we further introduce \textbf{DREAM} (\textit{\textbf{D}isentangling \textbf{R}isks to \textbf{E}nhance Safety \textbf{A}lignment in \textbf{M}LLMs}), a novel approach that enhances safety alignment in MLLMs through supervised fine-tuning and iterative Reinforcement Learning from AI Feedback (RLAIF). Experimental results show that DREAM significantly boosts safety during both inference and training phases without compromising performance on normal tasks (namely oversafety), achieving a 16.17% improvement in the SIUO safe&effective score compared to GPT-4V.

DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models

Self-consistency (SC), a widely used decoding strategy for chain-of-thought reasoning, shows significant gains across various multi-step reasoning tasks but comes with a high cost due to multiple sampling with the preset size. Its variants, Adaptive self-consistency (ASC) and Early-stopping self-consistency (ESC), dynamically adjust the number of samples based on the posterior distribution of a set of pre-samples, reducing the cost of SC with minimal impact on performance. Both methods, however, do not exploit the prior information about question difficulty. It often results in unnecessary repeated sampling for easy questions that could be accurately answered with just one attempt, wasting resources. To tackle this problem, we propose Difficulty-Adaptive Self-Consistency (DSC), which leverages the difficulty information of batch queries from both prior and posterior perspectives to adaptively allocate inference resources, further reducing the overall cost of SC. To demonstrate the effectiveness of DSC, we conduct extensive experiments on three popular categories of reasoning tasks: arithmetic, commonsense and symbolic reasoning on six benchmarks. The empirical results show that DSC consistently surpasses the strong baseline ASC and ESC in terms of costs by a significant margin, while attaining comparable performances.

Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning

The rise of Large Language Models (LLMs) revolutionizes information retrieval, allowing users to obtain required answers through complex instructions within conversations. However, publicly available services remain inadequate in addressing the needs of faculty and students to search campus-speciﬁc information. It is primarily due to the LLM’s lack of domain-speciﬁc knowledge and the limitation of search engines in supporting multilingual and timely scenarios. To tackle these challenges, we introduce ALOHA, a multilingual agent enhanced by hierarchical retrieval for university orientation. We also integrate external APIs into the front-end interface to provide interactive service. The human evaluation and case study show our proposed system has strong capabilities to yield correct, timely, and user-friendly responses to the queries in multiple languages, surpassing commercial chatbots and search engines. The system has been deployed and has provided service for more than 12,000 people.

ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval

Generated texts from large language models (LLMs) have been shown to exhibit a variety of harmful, human-like biases against various demographics. These findings motivate research efforts aiming to understand and measure such effects. This paper introduces a causal formulation for bias measurement in generative language models. Based on this theoretical foundation, we outline a list of desiderata for designing robust bias benchmarks. We then propose a benchmark called OccuGender, with a bias-measuring procedure to investigate occupational gender bias. We test several state-of-the-art open-source LLMs on OccuGender, including Llama, Mistral, and their instruction-tuned versions. The results show that these models exhibit substantial occupational gender bias. Lastly, we discuss prompting strategies for bias mitigation and an extension of our causal formulation to illustrate the generalizability of our framework.

Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias

Large language models (LLM) have shown remarkable abilities in text generation, question answering, language translation, reasoning and many other tasks. It continues to advance rapidly and is becoming increasingly influential in various fields, from technology and business to education and entertainment. Despite LLM's success in multiple areas, its ability to play abstract games, such as chess, is underexplored. Chess-playing requires the language models to output legal and reasonable moves from textual inputs. Here, we propose the Large language model ChessLLM to play full chess games. We transform the game into a textual format with the best move represented in the Forsyth-Edwards Notation. We show that by simply supervised fine-tuning, our model has achieved a professional-level Elo rating of 1788 in matches against the standard Elo-rated Stockfish when permitted to sample 10 times. We further show that data quality is important. Long-round data supervision enjoys a 350 Elo rating improvement over short-round data.

Complete Chess Games Enable LLM Become A Chess Master

The primary objective of Optical Chemical Structure Recognition is to identify chemical structure images into corresponding markup sequences. However, the complex two-dimensional structures of molecules, particularly those with rings and multiple branches, present significant challenges for current end-to-end methods to learn one-dimensional markup directly. To overcome this limitation, we propose a novel Ring-Free Language (RFL), which utilizes a divide-and-conquer strategy to describe chemical structures in a hierarchical form. RFL allows complex molecular structures to be decomposed into multiple parts, ensuring both uniqueness and conciseness while enhancing readability. This approach significantly reduces the learning difficulty for recognition models. Leveraging RFL, we propose a universal Molecular Skeleton Decoder (MSD), which comprises a skeleton generation module that progressively predicts the molecular skeleton and individual rings, along with a branch classification module for predicting branch information. Experimental results demonstrate that the proposed RFL and MSD can be applied to various mainstream methods, achieving superior performance compared to state-of-the-art approaches in both printed and handwritten scenarios. The code will be made publicly available.

RFL: Simplifying Chemical Structure Recognition with Ring-Free Language

Neural video compression has recently demonstrated significant potential to compete with conventional video codecs in terms of rate-quality performance. These learned video codecs are however associated with various issues related to decoding complexity (for autoencoder-based methods) and/or system delays (for implicit neural representation (INR) based models), which currently prevent them from being deployed in practical applications. In this paper, targeting a practical neural video codec, we propose a novel INR-based coding framework, PNVC, which innovatively combines autoencoder-based and overfitted solutions. Our approach benefits from several design innovations, including a new structural reparameterization-based architecture, hierarchical quality control, modulation-based entropy modeling, and scale-aware positional embedding. Supporting both low delay (LD) and random access (RA) configurations, PNVC outperforms existing INR-based codecs, achieving nearly 35\%+ BD-rate savings against HEVC HM 18.0 (LD) - almost 10\% more compared to one of the state-of-the-art INR-based codecs, HiNeRV and 5\% more over VTM 20.0 (LD), while maintaining 20+ FPS decoding speeds for 1080p content. This represents an important step forward for INR-based video coding, moving it towards practical deployment. The source code will be available for public evaluation.

PNVC: Towards Practical INR-based Video Compression

There are two manifestations of classification fairness. One is the preference for head classes with more instances due to the long-tail (LT) distribution of training data. The other is the clever Hans (CH) effect, where non-discriminative features are mistakenly used for classification. In this paper, we find that using category-agnostic zero-valued data can simultaneously reveal both types of unfairness. Based on this, we propose a zero uniformity training (ZUT) framework to optimize classification fairness. The ZUT framework inputs category-agnostic zero-valued data into the model in parallel and uses zero uniformity loss (ZUL) to optimize classification fairness. The ZUL loss mitigates bias towards specific classes by unifying the classification features corresponding to zero-valued data. The ZUT framework is compatible with various classification-based tasks. Experiments show that the ZUT framework can improve the performance of multiple state-of-the-art methods in image classification, person re-identification, and semantic segmentation.

Fair Training with Zero Inputs

Existing face forgery detection methods achieve promising performance when training and testing forgery data are from identical manipulation types, while they fail to generalize well to unseen samples. In this paper, we experimentally investigate and find that the poor generalization of the methods mainly arises from their overfitting on the known fake patterns. Excessively focused on seen fakes, those detectors fail to effectively learn image-intrinsic information and the distributional disparity between real and fake images. Then, to address this issue, we redefine fake learning as real-fake distributional disparity learning. We propose a novel deepfake detection framework learning distributional disparity based on the differentiated reconstruction on real and fake images for improved generalization. Specifically, distributional disparity learning on differentiated reconstruction of the real and fake images, enforces the model to learn image-invariant intrinsic representations. The reconstruction on real and fake images forces the decoders to learn the distribution of real and fake images, respectively. Moreover, to avoid the influence from the specificalization of the known fake patterns, we further propose the information interaction learning on the encoded intrinsic information and the pixel disparity between the input image and its reconstruction to distinguish face forgeries that are even unknown. Extensive experiments on large-scale benchmark datasets demonstrated the effectiveness of addressing the overfitting issue of the classification network, and verified the superior performance of our method.

FakeDiffer: Distributional Disparity Learning on Differentiated Reconstruction for Face Forgery Detection

The perception system for autonomous driving generally requires to handle multiple diverse sub-tasks. However, current algorithms typically tackle individual sub-tasks separately, which leads to low efficiency when aiming at obtaining full-perception results. Some multi-task learning methods try to unify multiple tasks with one model, but do not solve the conflicts in multi-task learning. In this paper, we propose M3Net, a novel multimodal and multi-task network that simultaneously tackles detection, segmentation, and 3D occu-
pancy prediction for autonomous driving and achieve superior performance than single task model. M3Net takes multimodal data as input and integrates multiple tasks via query-token interactions. To enhance the integration of multi-modal features
for multi-task learning, we first propose the Task-Adaptive Feature Integration (TAFI) module, which enables single-modality features to predict channel-wise attention weights for their high-performing tasks, respectively. Based on integrated features, we develop task-specific query initialization strategies to accommodate the needs of detection/segmentation and 3D occupancy prediction. Leveraging the properly initialized queries, a shared decoder transforms queries and BEV features in a layer-wise manner, facilitating multi-task learning. Furthermore, we propose a Task-oriented Channel Scaling (TCS) module in the decoder to mitigate conflicts between optimizing for different tasks. Additionally, our proposed multi-task querying and TCS module support both Transformer-based decoder and Mamba-based decoder, demonstrating its flexibility to different architectures. M3Net achieves state-of-the-art multi-task learning performance on the nuScenes benchmarks.

M3Net: Multimodal Multi-task Learning for 3D Detection, Segmentation, and Occupancy Prediction in Autonomous Driving

Object detection in Unmanned Aerial Vehicle (UAV) images has emerged as a focal area of research, which presents two significant challenges: i) objects are typically small and dense within vast images; ii) computational resource constraints render most models unsuitable for real-time deployment. Current real-time object detectors are not optimized for UAV images, and complex methods designed for small object detection often lack real-time capabilities. To address these challenges, we propose a novel detector, RemDet (Reparameter efficient multiplication Detector). Our contributions are as follows: 1) Rethinking the challenges of existing detectors for small and dense UAV images, and proposing information loss as a design guideline for efficient models. 2) We introduce the ChannelC2f module to enhance small object detection performance, demonstrating that high-dimensional representations can effectively mitigate information loss. 3) We design the GatedFFN module to provide not only strong performance but also low latency, effectively addressing the challenges of real-time detection. Our research reveals that GatedFFN, through the use of multiplication, is more cost-effective than feed-forward networks for high-dimensional representation. 4) We propose the CED module, which combines the advantages of ViT and CNN downsampling to effectively reduce information loss. It specifically enhances context information for small and dense objects. Extensive experiments on large UAV datasets, Visdrone and UAVDT, validate the real-time efficiency and superior performance of our methods. On the challenging UAV dataset VisDrone, our methods not only provided state-of-the-art results, improving detection by more than 3.4%, but also achieve 110 FPS on a single 4090.

RemDet: Rethinking Efficient Model Design for UAV Object Detection

Multimodal tracking has garnered widespread attention as a result of its ability to effectively address the inherent limitations of traditional RGB tracking. However, existing multimodal trackers mainly focus on the fusion and enhancement of spatial features or merely leverage the sparse temporal relationships between video frames. These approaches do not fully exploit the temporal correlations in multimodal videos, making it difficult to capture the dynamic changes and motion information of targets in complex scenarios. To alleviate this problem, we propose a unified multimodal spatial-temporal tracking approach named STTrack. In contrast to previous paradigms that solely relied on updating reference information, we introduced a temporal state generator (TSG) that continuously generates a sequence of tokens containing multimodal temporal information. These temporal information tokens are used to guide the localization of the target in the next time state, establish long-range contextual relationships between video frames, and capture the temporal trajectory of the target. Furthermore, at the spatial level, we introduced the mamba fusion and background suppression interactive (BSI) modules. These modules establish a dual-stage mechanism for coordinating information interaction and fusion between modalities. Extensive comparisons on five benchmark datasets illustrate that STTrack achieves state-of-the-art performance across various multimodal tracking scenarios.

Exploiting Multimodal Spatial-temporal Patterns for Video Object Tracking

Despite the rapid advancements in text-to-image (T2I) synthesis, enabling precise visual control remains a significant challenge. Existing works attempted to incorporate multi-facet controls (text and sketch), aiming to enhance the creative control over generated images. However, our pilot study reveals that the expressive power of humans far surpasses the capabilities of current methods. Users desire a more versatile approach that can accommodate their diverse creative intents, ranging from controlling individual subjects to manipulating the entire scene composition. We present VersaGen, a generative AI agent that enables versatile visual control in T2I synthesis. VersaGen admits four types of visual controls: i) single visual subject; ii) multiple visual subjects; iii) scene background; iv) any combination of the three above or merely no control at all. We train an adaptor upon a frozen T2I model to accommodate the visual information into the text-dominated diffusion process. We introduce three optimization strategies during the inference phase of VersaGen to improve generation results and enhance user experience. Comprehensive experiments on COCO and Sketchy validate the effectiveness and flexibility of VersaGen, as evidenced by both qualitative and quantitative results.

VersaGen: Unleashing Versatile Visual Control for Text-to-Image Synthesis

Text-to-Image generation (TTI) technologies are advancing rapidly, especially in the English language communities. However, apart from the user input language barrier problem, English-native TTI models inherently carry biases from their English world centric training data, which creates a dilemma for development of other language-native TTI models. One common choice is to fine-tune the English-native TTI model with translated samples. It falls short of fully addressing the model bias problem. Alternatively, training non-English language native models from scratch can effectively resolve the English world bias, but model trained this way would diverge from the English TTI communities, thus not able to utilize the strides continuously gaining in the English TTI communities any more. To build Chinese TTI model meanwhile keep compatibility with the English TTI communities, we propose a novel model structure referred as "Bridge Diffusion Model" (BDM). The proposed BDM employs a backbone-branch network structure to learn the Chinese semantics while keep the latent space compatible with the English-native TTI backbone, in an end-to-end manner. The unique advantages of the proposed BDM are that it's not only adept at generating images that precisely depict Chinese semantics, but also compatible with various English-native TTI plugins, such as different checkpoints, LoRA, ControlNet, Dreambooth, and Textual Inversion, \etc. Moreover, BDM can concurrently generate content seamlessly combining both Chinese-native and English-native semantics within a single image, fostering cultural interaction.

Bridge Diffusion Model: Bridge Chinese Text-to-Image Diffusion Model with English Communities

Recently, Gaussian Splatting has sparked a new trend in the field of computer vision. Apart from novel view synthesis, it has also been extended to the area of multi-view reconstruction. The latest methods facilitate complete, detailed surface reconstruction while ensuring fast training speed. However, these methods still require dense input views, and their output quality significantly degrades with sparse views. We observed that the Gaussian primitives tend to overfit the few training views, leading to noisy floaters and incomplete reconstruction surfaces. In this paper, we present an innovative sparse-view reconstruction framework that leverages intra-view depth and multi-view feature consistency to achieve remarkably accurate surface reconstruction. Specifically, we utilize monocular depth ranking information to supervise the consistency of depth distribution within patches and employ a smoothness loss to enhance the continuity of the distribution. To achieve finer surface reconstruction, we optimize the absolute position of depth through multi-view projection features. Extensive experiments on DTU and BlendedMVS demonstrate that our method outperforms state-of-the-art methods with a speedup of 60x to 200x, achieving swift and fine-grained mesh reconstruction without the need for costly pre-training.

FatesGS: Fast and Accurate Sparse-View Surface Reconstruction Using Gaussian Splatting with Depth-Feature Consistency

Recently, there has been considerable exploration of methods for generating 3D point clouds, which is crucial for numerous 3D vision applications. Though conditional generation methods showing a promising performance, it depends on the additional paired label. On the other hand, unconditional generation methods usually fail to annotate the generated 3D point cloud. In this paper, we introduce a novel self-conditional architecture that trains on unlabeled data and then generates high-quality labeled 3D point clouds. Specifically, we design a module to extract geometry and view features, and then use a feature fusion module to integrate them as a substitute for label embedding in conditional point cloud generation. Then the point cloud generator is trained using the fused features. LPCG also harnesses CLIP to handle the view features of point clouds for generating label information. Besides, we train two feature diffusion modules to capture the essence of multimodal features and obtain diverse fused features for use as conditions in generating point clouds. Experiments on the ShapeNet dataset demonstrate that LPCG achieves state-of-the-art performance for single class generation . Our experimental results show that the accuracy of our generated label annotations reaches around 97.44\% for a two-class generation task.

LPCG: A Self-conditional Architecture for Labeled Point Cloud Generation

Point cloud completion aims to recover a complete point shape from a partial point cloud. Although existing methods can form satisfactory point clouds in global completeness, they often lose the original geometry details and face the problem of geometric inconsistency between existing point clouds and reconstructed missing parts. To tackle this problem, we introduce {\bf SymmCompletion}, a highly effective completion method based on symmetry guidance. Our method comprises two primary components: a Local Symmetry Transformation Network (LSTNet) and a Symmetry-Guidance Transformer (SGFormer). First, LSTNet efficiently estimates point-wise local symmetry transformation to transform key geometries of partial inputs into missing regions, thereby generating geometry-align partial-missing pairs and initial point clouds. Second, SGFormer leverages the geometric features of partial-missing pairs as the explicit symmetric guidance that can constrain the refinement process for initial point clouds. As a result, SGFormer can exploit provided priors to form high-fidelity and geometry-consistency final point clouds. Qualitative and quantitative evaluations on several benchmark datasets demonstrate that our method outperforms state-of-the-art completion networks.

SymmCompletion: High-Fidelity and High-Consistency Point Cloud Completion with Symmetry Guidance

In recent years, reconstructing indoor scene geometry from multi-view images has achieved encouraging accomplishments. Current methods incorporate monocular priors into neural implicit surface models to achieve high-quality reconstructions. However, these methods require hundreds of images for scene reconstruction. When only a limited number of views are available as input, the performance of monocular priors deteriorates due to scale ambiguity, leading to the collapse of the reconstructed scene geometry. In this paper, we propose a new method, named Sparis, for indoor surface reconstruction from sparse views. Specifically, we investigate the impact of monocular priors on sparse scene reconstruction, introducing a novel prior based on inter-image matching information. Our prior offers more accurate depth information while ensuring cross-view matching consistency. Additionally, we employ an angular filter strategy and an epipolar matching weight function, aiming to reduce errors due to view matching inaccuracies, thereby refining the inter-image prior for improved reconstruction accuracy. The experiments conducted on widely used benchmarks demonstrate superior performance in sparse-view scene reconstruction.

Sparis: Neural Implicit Surface Reconstruction of Indoor Scenes from Sparse Views

Large Language Models (LLMs) have demonstrated potential in Vision-and-Language Navigation (VLN) tasks, yet current applications face challenges. While LLMs excel in general conversation scenarios, they struggle with specialized navigation tasks, yielding sub-optimal performance compared to specialized VLN models. We introduce FLAME (FLAMingo-Architectured Embodied Agent), a novel multimodal LLM-based agent and architecture designed for urban VLN tasks that efficiently handles multiple observations. Our approach implements a three-phase tuning technique for effective adaptation to navigation tasks, including single perception tuning for street view description, multiple perception tuning for trajectory summarization, and end-to-end training on VLN datasets. The augmented datasets are synthesized automatically. Experimental results demonstrate FLAME's superiority over existing methods, surpassing state-of-the-art methods by a 7.3\% increase in task completion rate on Touchdown dataset. This work showcases the potential of multimodal LLMs (MLLMs) in complex navigation tasks, representing an advancement towards practical applications of MLLMs in embodied AI. Codes will be made publicly available upon acceptance.

FLAME: Learning to Navigate with Multimodal LLM in Urban Environments

Extracting lane topology from perspective views (PV) is crucial for planning and control in autonomous driving. This approach extracts potential drivable trajectories for self-driving vehicles without relying on high-definition (HD) maps. However, the unordered nature and weak long-range perception of the DETR-like framework can result in misaligned segment endpoints and limited topological prediction capabilities. Inspired by the learning of contextual relationships in language models, the connectivity relations in roads can be characterized as explicit topology sequences. In this paper, we introduce $\textbf{Topo2Seq}$, a novel approach for enhancing topology reasoning via topology sequences learning. The core concept of Topo2Seq is a randomized order prompt-to-sequence learning between lane segment decoder and topology sequence decoder. The dual-decoder branches simultaneously learn the lane topology sequences extracted from the Directed Acyclic Graph (DAG) and the lane graph containing geometric information. Randomized order prompt-to-sequence learning extracts unordered key points from the lane graph predicted by the lane segment decoder, which are then fed into the prompt design of the topology sequence decoder to reconstruct an ordered and complete lane graph. In this way, the lane segment decoder learns powerful long-range perception and accurate topological reasoning from the topology sequence decoder. Notably, topology sequence decoder is only introduced during training and does not affect the inference efficiency. Experimental evaluations on the OpenLane-V2 dataset demonstrate the state-of-the-art performance of Topo2Seq in topology reasoning.

Topo2Seq: Enhanced Topology Reasoning via Topology Sequence Learning

LiDAR-based 3D object detection is crucial for autonomous driving. However, due to the quality deterioration of LiDAR point clouds, it suffers from performance degradation in adverse weather conditions. Fusing LiDAR with the weather-robust 4D radar sensor is expected to solve this problem; however, it faces challenges of significant differences in terms of data quality and the degree of degradation in adverse weather. To address these issues, we introduce L4DR, a weather-robust 3D object detection method that effectively achieves LiDAR and 4D Radar fusion. Our L4DR proposes Multi-Modal Encoding (MME) and Foreground-Aware Denoising (FAD) modules to reconcile sensor gaps, which is the first exploration of the complementarity of early fusion between LiDAR and 4D radar. Additionally, we design an Inter-Modal and IntraModal ({IM} 2 ) parallel feature extraction backbone coupled with a Multi-Scale Gated Fusion (MSGF) module to counteract the varying degrees of sensor degradation under adverse weather conditions. Experimental evaluation on a VoD dataset with simulated fog proves that L4DR is more adaptable to changing weather conditions. It delivers a significant performance increase under different fog levels, improving the 3D mAP by up to 20.0% over the traditional LiDAR-only approach. Moreover, the results on the K-Radar dataset validate the consistent performance improvement of L4DR in real-world adverse weather conditions.

L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection

Optical Coherence Tomography (OCT) is a widely adopted imaging technique in ophthalmology, yet its clinical interpretation remains prone to inconsistency and subjectivity. This study presents a retrieval-augmented generation (RAG) framework aimed at improving both diagnostic accuracy and interpretability in OCT-based analysis. The pipeline integrates a dual-headed convolutional neural network with a case-based retrieval system and a language model capable of generating structured clinical summaries. A ResNet-50 model was trained to classify diabetic retinopathy (DR), diabetic macular edema (DME), and key retinal biomarkers using a public OCT dataset. Feature embeddings generated by the model were indexed into a similarity-search database to enable retrieval of clinically relevant cases. Retrieved scans, along with classification outputs, were used to prompt a large language model to generate concise diagnostic reports that reflect both the model’s predictions and visually aligned precedents. The system achieved 89% accuracy in distinguishing DR from DME in a hold-out test set. Interpretability was also demonstrated through consistent retrieval of semantically similar cases. Generated reports were found to be readable and aligned with clinical reasoning. By combining classification performance with transparent, case-grounded explanations, the RAG approach enhances clinical confidence and supports decision-making. This work highlights the potential of retrieval-augmented pipelines to reduce variability in OCT interpretation and to serve as a scalable, explainable foundation for future AI-driven diagnostic support in ophthalmology.

Improving Optical Coherence Tomography Interpretation With Retrieval-Guided Diagnosis and Text Generation

Chronic kidney disease (CKD) is a progressive and irreversible loss of kidney function that affected about 753 million people globally in 2016. There are often no noticeable symptoms until it reaches later stages which makes early identification important for prevention. Diabetes mellitus is among the leading causes of chronic kidney disease worldwide, with an estimated 40\% of diabetic patients developing diabetic kidney disease. This project applies machine learning techniques to predict self-reported kidney disease risk in older diabetic individuals (ages 55+) using data from the U.S. National Health and Nutrition Examination Survey (NHANES). I used survey- based variables such as blood pressure, glycohemoglobin, body mass index (BMI), physical activity, and demographics to create a classification model. After preprocessing and engineering features related to age, blood pressure, and activity levels, I trained logistic regression and random forest classifiers to identify individuals at higher risk. The random forest model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.93, while the logistic regression model achieved an ROC AUC of 0.87. These results show that both models can effectively identify high-risk individuals. Feature importance analysis identified blood pressure, age, BMI, and diabetes status as leading predictors. This study demonstrates that machine learning can be used to improve early detection of CKD risk in aging diabetic populations.

Early Detection of Kidney Disease in Aging Diabetic Populations Using Machine Learning

The global rise in the elderly population is putting an increasing strain on healthcare systems, particularly due to the increased chance of chronic illness, polypharmacy, and healthcare utilization. The traditional one-size-fits-all treatment approach often fails to address the unique and evolving needs of older adults, leading to adverse drug reactions, diminished quality of life, and inefficient use of medical resources. This study investigated the potential of personalized medicine — augmented by artificial intelligence (AI) — to improve elderly care through data-driven, individualized treatment plans. By integrating data from electronic health records, genomics, wearable devices, and lifestyle factors, we developed AI-enhanced models capable of tailoring treatments to each patient’s specific profile. These models allow for real-time treatment adjustments, predictive risk assessments, and optimized drug regimens. Our findings suggest that AI-driven personalized care can reduce adverse outcomes, enhance clinical effectiveness, and support proactive, scalable, and patient-centered healthcare for aging populations.

Improving Prediction of ADR in Elderly Patients Using Machine Learning Algorithms

Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task. However, we found that inherent heterogeneity among factor tensors in tensor decomposition significantly hinders the tensor fusion process and further limits the performance of link prediction. To overcome this limitation, we introduce a novel method that maps factor tensors onto a unified smooth Lie group manifold to make the distribution of factor tensors approximating homogeneous in tensor decomposition. We provide the theoretical proof of our motivation that homogeneous tensors are more effective than heterogeneous tensors in tensor fusion and approximating the target for tensor decomposition based TKGE methods. The proposed method can be directly integrated into existing tensor decomposition based TKGE methods without introducing extra parameters. Extensive experiments demonstrate the effectiveness of our method in mitigating the heterogeneity and in enhancing the tensor decomposition based TKGE models.

Mitigating Heterogeneity among Factor Tensors via Lie Group Manifolds for Tensor Decomposition Based Temporal Knowledge Graph Embedding

Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interacting with environments through generating executable actions. This inadequacy primarily stems from the lack of built-in action knowledge in language agents, which fails to effectively guide the planning trajectories during task solving and results in planning hallucination. To address this issue, we introduce KnowAgent, a novel approach designed to enhance the planning capabilities of LLMs by incorporating explicit action knowledge. Specifically, KnowAgent employs an action knowledge base and a knowledgeable self-learning strategy to constrain the action path during planning, enabling more reasonable trajectory synthesis, and thereby enhancing the planning performance of language agents. Experimental results on HotpotQA and ALFWorld based on various backbone models demonstrate that KnowAgent can achieve comparable or superior performance to existing baselines. Further analysis indicates the effectiveness of KnowAgent in terms of planning hallucinations mitigation.

KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents

Speculative Decoding (SD) utilizes an efficient draft model to generate multiple tokens, which are subsequently verified in parallel by a target model. This approach has shown significant potential for accelerating inference in large language models (LLMs), with performance heavily reliant on the hyperparameter $K$—the window size. However, previous methods often depend on simple heuristics to select $K$ or dynamically adjust the window size, which may necessitate additional training or careful resource management to avoid competition. To address these challenges, we propose \textbf{H}ierarchical \textbf{S}peculative \textbf{D}ecoding with \textbf{D}ynamic \textbf{W}indow (HSDDW), a straightforward framework that eliminates the need for additional training. Specifically, we introduce a \textit{self-verify} mechanism that enables the draft model to autonomously decide when to stop generating tokens. Additionally, by integrating a hierarchical structure that leverages the capabilities of models of different sizes, we significantly enhance the overall speed of the system. HSDDW demonstrates competitive performance across four datasets, achieving notable speedups of $2.91\times$ on MT-Bench and $2.99\times$ on Alpaca, outperforming existing state-of-the-art methods.

Hierarchical Speculative Decoding with Dynamic Window

Effective organization of in-context learning (ICL) demonstrations is key to improving the quality of large language model (LLM) responses. To create better sample-label pairs that instruct LLM understanding, we introduce logit separability, a criterion to assess the clarity of both samples and class-related words at the logit level. This facilitates the optimization of sample and label selection, enhancing the precision of information provided in ICL demonstrations. Additionally, we find that incorporating multiple class-related words for each sample, rather than relying on a single class name, improves performance by offering a broader range of label information. Building on these insights, we propose LICL, a logit separability-based method that jointly organizes samples and integrates multiple class-related words into each sample-label pair. Evaluations across seven classification datasets show that this approach significantly improves ICL performance by providing clearer instructions and richer label information.

Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context Learning

In multi-agent scenarios, the ability to anticipate and respond to opponents is essential, particularly in environments involving adversarial and collaborative interactions. In this paper, we introduce Explicit Models of Opponents (EMO) based on Large Language Models (LLMs), enabling agents to better predict and adapt to diverse, dynamic multi-agent interactions. Unlike traditional methods that often simplify multi-agent interactions using a single opponent model, EMO constructs an individual model for each opponent and aligns these models working in synergy through a bi-level feedback-refinement framework. We test EMO alongside several reasoning methods in multi-player deduction games, where agents must infer hidden information about their opponents. The results show that EMO significantly enhances agents' decision-making, outperforming traditional single-model approaches. Our findings demonstrate that EMO can be a powerful tool for enhancing LLM-based agents in complex multi-agent systems.

LLM-Based Explicit Models of Opponents for Multi-Agent Games

Hypernetworks are a class of meta-networks that generate weights for main neural networks. Their unique parameter spaces necessitate exploring suitable optimization strategies to enhance performance, especially for language models. However, a comprehensive investigation into optimization strategies for hypernetworks remains absent. To address this gap, we analyze the loss landscape of hypernetworks and propose that restart optimization strategies can improve their performance for language models. We find that hypernetworks have inherently more complicated loss landscapes compared to conventional networks due to their distinct parameter spaces. Consequently, a restart strategy that periodically resets the learning rate can facilitate better convergence for hypernetworks. Through experiments on instruction tuning and multi-task training, we demonstrate that the restart strategy consistently enhances the performance of hypernetworks for language models, often more effectively than for conventional deep neural networks. Our findings highlight the importance of tailored optimization techniques to unlock the full potential of hypernetworks in natural language processing tasks.

Enhancing Language Model Hypernetworks with Restart: A Study on Optimization

In this work we systematically investigate how specific attributes of preference datasets affect the alignment and downstream performance of LLMs in instruction-following tasks. We use a novel synthetic data generation pipeline to generate 48,000 unique instruction-following prompts with combinations of 23 verifiable constraints that enable fine-grained and automated quality assessments of model responses. With our synthetic prompts, we use rejection sampling (RS) and Monte Carlo Tree Search (MCTS) to obtain preference pairs. Then, we perform experiments investigating the effects of (1) the presence of shared prefixes between the chosen and rejected responses, (2) the contrast and quality of the chosen, rejected responses and (3) the complexity of the training prompts. Our experiments reveal that shared prefixes provide marginal but consistent improvements and greater stability across challenging training configurations. While high-contrast preference pairs generally outperform low-contrast pairs, combining both often yields the best performance. Additionally, training on prompts of moderate difficulty leads to better generalization across different tasks. Our findings provide actionable insights into optimizing preference data curation for instruction-following tasks, offering a scalable and effective framework for enhancing LLM training and alignment.

A Systematic Examination of Preference Learning through the Lens of Instruction-Following

Cloud-based Large Language Models (LLMs) such as ChatGPT have become increasingly integral to daily operations. Nevertheless, they also introduce privacy concerns: firstly, numerous studies underscore the risks to user privacy posed by jailbreaking cloud-based LLMs; secondly, the LLM service providers have access to all user data, which deters individuals from confidently utilizing such services. To address such concerns, we propose a simple yet effective paradigm, **EmojiPrompt**, to protect user privacy. At its core, EmojiPrompt performs generative transformation, obfuscating private data within prompts with linguistic and non-linguistic elements before submitting them to cloud-based LLMs. We evaluate EmojiPrompt's performance across 8 datasets from various domains. We also propose simulated inference attacks to assess EmojiPrompt's ability to preserve user privacy. The results demonstrate that EmojiPrompt effectively obfuscates user private data, while largely maintaining, or even enhancing, performances compared to the unobfuscated version. Furthermore, EmojiPrompt's atomic-level obfuscation allows it to function exclusively with cloud-based LLMs. For source code, please refer to: https://github.com/agiresearch/EmojiCrypt.

EmojiPrompt: Generative Prompt Obfuscation for Privacy-Preserving Communication with Cloud-based LLMs

Entity matching (EM), which identifies whether two data records refer to the same real-world entity, is crucial for knowledge base construction and enhancing data-driven AI systems. Recent advances in language models (LMs) have shown great potential in resolving entities with rich textual attributes. However, their performance heavily depends on how structured entities are ''talked'' through serialized text. The impact of this serialization process remains underexplored, particularly for entities with complex relations in knowledge graphs (KGs). In this work, we systematically study entity serialization by benchmarking the effect of common schemes with LMs of different sizes on diverse tabular matching datasets. We apply our findings to propose a novel serialization scheme for KG entities based on random walks and utilize LLMs to encode sampled semantic walks for matching. Using this lightweight approach with open-source LLMs, we achieve a leading performance on EM in canonical and highly heterogeneous KGs, demonstrating significant throughput increases and superior robustness compared to GPT-4-based methods. Our study on serialization provides valuable insights for the deployment of LMs in real-world EM tasks.

How to Talk to Language Models: Serialization Strategies for Structured Entity Matching

One of the key tasks in machine learning for tabular data is feature engineering. Although it is vital for improving the performance of models, it demands considerable human expertise and deep domain knowledge, making it labor-intensive endeavor. To address this issue, we propose a novel framework, $\textbf{FeRG-LLM}$ ($\textbf{Fe}$ature engineering by $\textbf{R}$eason $\textbf{G}$eneration $\textbf{L}$arge $\textbf{L}$anguage $\textbf{M}$odels), a large language model designed to automatically perform feature engineering at an 8-billion-parameter scale. We have constructed two-stage conversational dialogues that enable language models to analyze machine learning tasks and discovering new features, exhibiting their Chain of Thoughts (CoT) capabilities. We use these dialogues to fine-tune Llama 3.1 8B model and integrate Direct Preference Optimization (DPO) to receive feedback improving quality of new features and the model's performance. Our experiments show that FeRG-LLM performs comparably to or better than Llama 3.1 70B on most datasets, while using fewer resources and achieving reduced inference time. It outperforms other studies in classification tasks and performs well in regression tasks. Moreover, since it does not rely on cloud-hosted LLMs like GPT-4 with extra API costs when generating features, it can be deployed locally, addressing security concerns.

FeRG-LLM : Feature Engineering by Reason Generation Large Language Models

Generative replay has proven effective in addressing the catastrophic forgetting issue of continual learning (CL) in natural language processing (NLP). However, relying on a single task-specific token or prompt often falls short in generating pseudo-samples that accurately reflect the true data distribution. This leads to issues of semantic inconsistency and scale inconsistency. To tackle these challenges, we propose a Prototype Conditioned Generative Replay (PCGR) method, which enhances generative reply by incorporating task-level statistics through a Prototype Conditioned Variational Autoencoder (PCVAE). Specifically, task-level embedding statistics are stored as prototypes for each old task. When a new task is introduced, PCVAE draws samples from task-specific prototype-based distributions to generate pseudo-samples. By incorporating the prototype, the generated pseudo-samples are both more representative and sufficiently diverse to reflect the real data distribution. Furthermore, as previously stored prototypes may become outdated due to evolving model parameters, we propose a Prototype Shift Estimation (PSE) to adjust for these changes. Experiments on NLP tasks across two different scenarios show that PCGR outperforms previous state-of-the-art (SOTA) methods.

Prototype Conditioned Generative Replay for Continual Learning in NLP

Long-context Multimodal Large Language Models (MLLMs) that incorporate long text-image and text-video modalities, demand substantial computational resources as their multimodal Key-Value (KV) cache grows with increasing input lengths, challenging memory and time efficiency. For multimodal scenarios, the cross-modal interactions inevitablely increase complexity, and prior methods for KV cache compression, in both text-only and multimodal LLMs, have neglected attention density variations across layers, often adopting uniform or progressive reduction strategis for layer-wise cache allocation. This results in precision loss and suboptimal performance. We propose \textbf{MEDA}, a novel approach specifically designed for the complexities of multimodal settings, dynamically allocating KV cache sizes based on attention entropy to better adapt to multimodal interactions.Through a dynamic multimodal KV cache allocation strategy, MEDA compresses the KV cache, adaptively retains sufficient multimodal information at each layer. Meanwhile, to mitigate the degradation of contextual information due to cache compression, we also integrate KV pairs merging techniques to maintain coherence. MEDA achieves up to \textbf{72\%} KV cache memory reduction and \textbf{2.82} faster decoding speeds in some cases, while maintaining or enhancing performance on various multimodal tasks in a long context, including multi-image and long video scenarios.

MEDA: Dynamic KV Cache Allocation for Efficient Multimodal Long-Context Inference

Recent advancements in Large Language Models (LLMs) have set themselves apart with their exceptional performance in complex language modelling tasks. However, these models are also known for their significant computational and storage requirements, primarily due to the quadratic computation complexity of softmax attention. To mitigate this issue, linear attention has been designed to reduce the quadratic space-time complexity that is inherent in standard transformers. In this work, we embarked on a comprehensive exploration of three key components that substantially impact the performance of the Gated Linear Attention module: feature maps, normalization, and the gating mechanism. We developed a feature mapping function to address some crucial issues that previous suggestions overlooked. Then we offered further rationale for the integration of normalization layers to stabilize the training process. Moreover, we explored the saturation phenomenon of the gating mechanism and augmented it with a refining module. We conducted extensive experiments and showed our architecture outperforms previous Gated Linear Attention mechanisms in extensive tasks including training from scratch and post-linearization with continual pre-training.

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