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Bargaining is often regarded as a logical arena rather than an art or a matter of intuition, yet Large Language Models (LLMs) still struggle to navigate it due to limited strategic depth and difficulty adapting to complex human factors. Current benchmarks rarely capture this limitation. To bridge this gap, we present a utility feedback centric framework. Our contributions are: (i) AgoraBench, a new benchmark spanning nine challenging settings (e.g., deception, monopoly) that supports diverse strategy modeling; (ii) human-aligned, economically grounded metrics derived from utility theory. This is operationalized via agent utility, negotiation power, and acquisition ratio that implicitly measure how well the negotiation aligns with human preference and (iii) a human preference grounded dataset with learning pipeline that strengthens LLMs’ bargaining ability through both prompting and finetuning. Empirical results indicate that baseline LLM strategies often diverge from human preferences, while our mechanism substantially improves negotiation performance, yielding deeper strategic behavior and stronger opponent awareness.

ACL 2026

MERIT Feedback Elicits Better Bargaining in LLM Negotiators

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).

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AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

poster

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling lesion implantation/removal that threatens clinical trust and safety. Existing defenses are inadequate for healthcare. Medical detectors are largely black-box, while MLLM-based explainers are typically post-hoc, lack medical expertise, and may hallucinate evidence on ambiguous cases. We present MedForge, a data-and-method solution for pre-hoc, evidence-grounded medical forgery detection. We introduce MedForge-90K, a large-scale benchmark of realistic lesion edits across 19 pathologies with expert-guided reasoning supervision via doctor inspection guidelines and gold edit locations. Building on it, MedForge-Reasoner performs localize-then-analyze reasoning, predicting suspicious regions before producing a verdict, and is further aligned with Forgery-aware GSPO to strengthen grounding and reduce hallucinations. Experiments demonstrate state-of-the-art detection accuracy and trustworthy, expert-aligned explanations.

MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning

As multimodal large language models (MLLMs) have advanced to process video inputs, concerns have emerged about their potential for malicious misuse. Prior jailbreak studies have shown that safety alignment in MLLMs can be bypassed through visual inputs, yet it remains unclear which properties of video inputs induce this vulnerability. To address this gap, we introduce Multi-Clip Video (MCV) SafetyBench, a dataset of 2,920 videos designed to evaluate how the diversity of video inputs affects the vulnerability of MLLMs. Each video consists of multiple short clips depicting diverse contexts related to a harmful query. Experiments on eight representative video MLLMs show that attack success consistently increases with the number of clips. Our results further indicate that the video modality is (1) more vulnerable than the image modality, (2) more vulnerable to dynamic videos than to static videos, and (3) more vulnerable when videos contain more diverse contexts. Building on these findings, we propose a defense strategy that leverages the relative robustness of the image modality. Warning: This paper may contain potentially offensive content.

Jailbreaking Multimodal Large Language Models using Multi-Clip Video

The quadratic complexity of attention mechanisms poses a critical bottleneck for large language models processing long contexts. While dynamic sparse attention methods offer input-adaptive efficiency, they face fundamental trade-offs: requiring preprocessing, lacking global evaluation, violating query independence, or incurring high computational overhead. We present RRAttention, a novel dynamic sparse attention method that simultaneously achieves all desirable properties through a head **r**ound-**r**obin (RR) sampling strategy. By rotating query sampling positions across attention heads within each stride, RRAttention maintains query independence while enabling efficient global pattern discovery with stride-level aggregation. Our method reduces complexity from $O(L^2)$ to $O(L^2/S^2)$ and employs adaptive Top-$\tau$ selection for optimal sparsity. Extensive experiments on natural language understanding (HELMET) and multimodal video comprehension (Video-MME) demonstrate that RRAttention recovers over 99\% of full attention performance while computing only half of the attention blocks, achieving 2.4$\times$ speedup at 128K context length and outperforming existing dynamic sparse attention methods. The code is available at [https://github.com/PaddlePaddle/PaddleFleet](https://github.com/PaddlePaddle/PaddleFleet) (see `Research/RRAttention`).

 RRAtention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference

Automating Graphical User Interface (GUI) operations with Multimodal Large Language Models (MLLMs) is promising but remains bottlenecked in real-world long-horizon settings. Key challenges include ensuring precise grounding across diverse interfaces and handling irreversible errors in extended workflows. Current methods often struggle to distinguish targets in low Signal-to-Noise Ratio (SNR) environments and lack sufficient pre-execution verification to prevent error accumulation. To address this, we propose the Memory-augmented Debate System (MaDS). Specifically, MaDS combines: (1) a Dual-Layer Memory Module that integrates universal interaction priors with scenario-specific operational experience to mitigate grounding hallucinations; and (2) Multi-Round Debate that performs pre-execution verification, while transforming execution failures into retrievable Negative Warnings to reduce repeated errors. Additionally, we introduce MaDS-Benchmark, a benchmark for long-horizon mobile GUI tasks with process-oriented evaluation. Experiments show that MaDS achieves a 90.23% Task Success Rate on MaDS-Benchmark and strong performance on public benchmarks including AITW, AITZ, CAGUI, and GUIOdyssey.

MaDS: Long-Horizon GUI Automation via Synergizing Dual-Layer Memory and Multi-Round Debate

Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally support format-constrained tasks like parseable JSON or reasoning templates. While straightforward fixed anchors can enforce such constraints, they often impose rigid spans, leading to truncated reasoning or redundant content. To overcome this, we propose Dynamic Infilling Anchors (DIA), a training-free method that dynamically estimates end-anchor positions to adjust generation length before iterative infilling. This flexible mechanism ensures structural correctness and semantic coherence, avoiding the inefficiencies of fixed-span methods. Experiments on reasoning benchmarks demonstrate that DIA substantially improves format compliance and answer accuracy, achieving significant zero-shot gains on GSM8K and MATH. These results establish DIA as a robust pathway toward reliable, structure-aware generation.

Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models

Vision-language models (VLMs) are increasingly used to make visual content accessible via text-based descriptions. In current systems, however, description specificity is often conflated with their length. We argue that these two concepts must be disentangled: descriptions can be concise yet dense with information, or lengthy yet vacuous. We define specificity relative to a contrast set, where a description is more specific to the extent that it picks out the target image better than other possible images. We construct a dataset that controls for length while varying information content, and validate that people reliably prefer more specific descriptions regardless of length. We find that controlling for length alone cannot account for differences in specificity; it matters how the length budget is applied. These results support evaluation approaches that directly prioritize specificity over verbosity.

 When More Words Say Less: Decoupling Length and Specificity in Image Description Evaluation

Interleaved-Modal Chain-of-Thought (I-MCoT) advances vision-language reasoning, such as Visual Question Answering (VQA). This paradigm integrates specially selected visual evidence from the input image into the context of Vision-Language Models (VLMs), enabling them to ground their reasoning logic in these details. Accordingly, the efficacy of an I-MCoT framework relies on identifying *what* to see (evidence selection) and *when* to see it (triggering of insertions). However, existing methods fall short in both aspects. First, for selection, they rely on attention signals, which are unreliable---particularly under severe granularity imbalance between the brief textual query and the informative image. Second, for triggering, they adopt static triggers, which fail to capture the VLMs' dynamic needs for visual evidence. To this end, we propose a novel I-MCoT framework, **A**ctive **I**nformation-driven **M**ulti-modal **C**hain-**o**f-**T**hought (**AIM-CoT**), which aims to improve both evidence selection and insertion triggering via: (1) **Context-enhanced Attention-map Generation (CAG)** to mitigate granularity imbalance via textual context enhancement; (2) **Active Visual Probing (AVP)** to proactively select the most informative evidence via an information foraging process; and (3) **Dynamic Attention-shift Trigger (DAT)** to precisely activate insertions when VLM's attention shifts from text to visual context. Experiments across three benchmarks and four backbones demonstrate AIM-CoT's consistent superiority. Our code is available at https://anonymous.4open.science/r/AIMCoT.

AIM-CoT: Active Information-driven Multimodal Chain-of-Thought for Vision-Language Reasoning

Medical Multimodal Large Language Models (Med-MLLMs) require egocentric clinical intent understanding for real-world deployment, yet existing benchmarks fail to evaluate this critical capability. We introduce MedGaze-Bench, the first benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation, and diagnostic interpretation. Our benchmark addresses three fundamental challenges: visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical workflows, and implicit adherence to safety protocols. We propose a Three-Dimensional Clinical Intent Framework evaluating: (1) Spatial Intent—discriminating precise targets amid visual noise, (2) Temporal Intent—inferring causal rationale through retrospective and prospective reasoning, and (3) Standard Intent—verifying protocol compliance through safety checks. Beyond accuracy metrics, we introduce Trap QA mechanisms to stress-test clinical reliability by penalizing hallucinations and cognitive sycophancy. Experiments reveal current MLLMs struggle with egocentric intent due to over-reliance on global features, leading to fabricated observations and uncritical acceptance of invalid instructions.

Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models

Large language models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, yet their autoregressive generation paradigm makes it computationally expensive to explore diverse reasoning paths. In contrast, diffusion language models (DLMs) adopt a parallel, non-autoregressive generation mechanism that enables the efficient production of diverse candidate outputs. Motivated by this complementarity, we explore a collaborative reasoning framework that combines diffusion-based generation with autoregressive evaluation. Specifically, we leverage DLMs to efficiently generate diverse intermediate reasoning thoughts, and employ LLMs as evaluators to assess and select candidates based on their plausibility and correctness. By decoupling proposal generation from evaluation, our framework exploits the strengths of both models: efficient exploration from diffusion models and causally grounded assessment from autoregressive models, which naturally aligns with the divergent-convergent thinking framework in cognitive psychology. Experiments across various mathematical and logical reasoning benchmarks demonstrate that our framework improves inference efficiency while maintaining competitive or superior reasoning accuracy, laying the groundwork for building efficient reasoning architectures. Our code is open-source at https://anonymous.4open.science/r/Diffuse-Thinking-EC60.

Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for Reasoning

Graph structures are increasingly adopted in dialog memory systems, motivated by their success in retrieval-augmented generation and the associative nature of human memory. However, empirical findings on their effectiveness remain inconsistent, making it unclear which design choices truly matter. In this work, we present an experimental and system-oriented analysis of long-term dialog memory architectures. We formalize a unified framework that decomposes dialog memory systems into core components and supports both graph-based and non-graph approaches. Under this framework, we conduct controlled, stage-wise experiments on LongMemEval and HaluMem, comparing common design choices in memory representation, organization and maintenance, as well as indexing and retrieval. Our results show that underlying implementation details—often insufficiently specified in prior work—have a substantial impact on performance, and we identify stable, reliable strong baselines to support fair comparison and practical deployment. Code are available at https://github.com/AvatarMemory/UnifiedMem.

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MedForge: Interpretable Medical Deepfake Detection via Forgery-aware Reasoning

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