United States

Augmenting large language models (LLMs) with tools significantly enhances their problem-solving potential across multifaceted tasks.
However, current tools automatically created by LLMs often serve as a mere summary of specific problems or solutions, which face two main issues: 
1) Low reusability: The tools are overly problem-specific and struggle to handle new problems.
2) Limited diversity: The toolsets are too narrow, limiting their application to address a broader range of different problems.
In this paper, we propose the Knowledge-grounded Tool Creation with Evolution (KTCE) framework, which aims to craft reusable and comprehensive toolsets for LLMs in a two-stage process.
In the first stage (Knowledge-based Tool Creation), we conceptualize tools as a form of executable domain knowledge and propose a problem-knowledge-tool paradigm. 
Specifically, we leverage LLMs to abstract &quot;knowledge&quot; from &quot;problems&quot; and create a three-layer knowledge tree of topics, concepts, and key points. This hierarchical structure serves as a foundation for inducing atomic &quot;tools&quot; from &quot;knowledge&quot;, grounding them in fundamental concepts and enhancing their usability.
In the second stage (Tool Evolutionary Search), we evolve the toolsets through several actions including tool selection, mutation, and crossover. This stage mimics the biological evolution process, aiding toolsets in discovering new tools or updating existing ones, thereby increasing the diversity of the toolset.
Experiments on challenging mathematical/tabular/scientific reasoning tasks demonstrate that our approach achieves substantial accuracy improvements ranging from 5.02% to 17.62% on average. Moreover, in-depth analyses reveal the superior characteristics of our toolkit, including high reusability, high diversity, and high generalizability on cross-data/LLM performance with low complexity.

AAAI 2025

Automated Creation of Reusable and Diverse Toolsets for Enhancing LLM Reasoning

snlp

question answering

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. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Augmenting large language models (LLMs) with tools significantly enhances their problem-solving potential across multifaceted tasks.
However, current tools automatically created by LLMs often serve as a mere summary of specific problems or solutions, which face two main issues: 
1) Low reusability: The tools are overly problem-specific and struggle to handle new problems.
2) Limited diversity: The toolsets are too narrow, limiting their application to address a broader range of different problems.
In this paper, we propose the Knowledge-grounded Tool Creation with Evolution (KTCE) framework, which aims to craft reusable and comprehensive toolsets for LLMs in a two-stage process.
In the first stage (Knowledge-based Tool Creation), we conceptualize tools as a form of executable domain knowledge and propose a problem-knowledge-tool paradigm. 
Specifically, we leverage LLMs to abstract "knowledge" from "problems" and create a three-layer knowledge tree of topics, concepts, and key points. This hierarchical structure serves as a foundation for inducing atomic "tools" from "knowledge", grounding them in fundamental concepts and enhancing their usability.
In the second stage (Tool Evolutionary Search), we evolve the toolsets through several actions including tool selection, mutation, and crossover. This stage mimics the biological evolution process, aiding toolsets in discovering new tools or updating existing ones, thereby increasing the diversity of the toolset.
Experiments on challenging mathematical/tabular/scientific reasoning tasks demonstrate that our approach achieves substantial accuracy improvements ranging from 5.02% to 17.62% on average. Moreover, in-depth analyses reveal the superior characteristics of our toolkit, including high reusability, high diversity, and high generalizability on cross-data/LLM performance with low complexity.

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



We present a new formal framework for generalized planning (GP) based on the situation calculus extended with LTL constraints. The GP problem is specified by a first-order basic action theory whose models are the problem instances. This low-level theory is then abstracted into a high-level propositional nondeterministic basic action theory with a single model. A refinement mapping relates the two theories. LTL formulas are used to specify the temporally extended goals as well as assumed trace constraints. If all LTL trace constraints hold at the low level and the high-level model can simulate all the low-level models with respect to the mapping, we say that we have a temporally lifted abstraction. We prove that if we have such an abstraction and the agent has a strategy to achieve a LTL goal under some trace constraints at the abstract level, then there exists a refinement of the strategy to achieve the refinement of the goal at the concrete level. We use LTL synthesis to generate the strategy at the abstract level. We illustrate our approach by synthesizing a program that solves a data structure manipulation problem.

Situation Calculus Temporally Lifted Abstractions for Generalized Planning

Heterogeneous Graph Neural Networks (HGNNs) have achieved state-of-the-art performance in classifying molecular graphs, capitalizing on their ability to capture rich semantics. However, HGNNs for molecule property prediction exhibit significant susceptibility to adversarial attacks—a challenge that prior research has entirely overlooked. To fill this gap, this paper introduces the first study focused on robust graph-level representation learning tailored for heterogeneous molecular graphs. To achieve this goal, we propose a comprehensive $\textbf{R}$obust $\textbf{H}$eterogeneous $\textbf{G}$raph $\textbf{C}$lassification ($\textbf{RHGC}$) framework grounded in the Information Bottleneck principle, which aims to identify the most informative and least noisy heterogeneous subgraphs to derive robust, holistic representations.  This is specifically accomplished through a dedicated Node Semantic Purifier, which enhances node-level and semantic-level robustness by eliminating label-irrelevant interference using graph stochastic attention and the Hilbert-Schmidt Independence Criterion, along with a Global Graph Disentanglement method, which improves graph-level robustness by addressing information leak. Experiments on three molecular benchmarks demonstrate that RHGC enhances accuracy by an average of 5.06% under all three attack settings and meanwhile by 4.33% on clean data. Our code and model will be made publicly available, which can be found in the supplementary material.

Robust Heterogeneous Graph Classification for Molecular Property Prediction with Information Bottleneck

We study the problem of designing replication-proof bandit mechanisms when agents strategically register or replicate their own arms to maximize their payoff.
Specifically, we consider Bayesian agents who only know the distribution from which their own arms' mean rewards are sampled, which is the first to study Bayesian extension of Shin et al. 2022.
With Bayesian agents, in stark contrast to the previous work, analyzing the replication-proofness of an algorithm becomes significantly complicated even in a single-agent setting.
We provide sufficient and necessary conditions for an algorithm to be replication-proof in the single-agent setting, and prove that exploration-then-commit algorithm satisfies these properties.
These results centers around several analytical theorems in comparing the expected regret of multiple bandit instances, which might be of independent interest and never have been studied to the best of our knowledge. 
We expand this result to the multi-agent setting, and provide a replication-proof algorithm for any problem instance.
We finalize our result by proving its sublinear regret upper bound that matches H-UCB.

Replication-proof Bandit Mechanism Design with Bayesian Agents

Proof systems can be used for certification of logic problems, and proof complexity can inform us how succinct certificates can be.  

In the PSPACE complete logic QBF (Quantified Boolean Formulas)  refutation proofs often contain information that reproduce the witnesses of the quantified variables. This is known as strategy extraction. There are two known kinds of strategy extraction for proof systems, local strategy extraction and round-based strategy extraction. Formalisation of local strategy extraction was done previously, in this paper we formalise round-based strategy extraction. 

By formalising the strategy extraction into circuits we can show new p-simulations. P-simulations are processes that allow you to transform proofs from a weaker proof system to a stronger proof system. Thus we solve an open problem in QBF proof complexity that Extended QBF Frege p-simulates LD-Q(\Drrs)-Resolution. LD-Q(\Drrs)-Resolution is the underlying proof system for the solver Qute.

This is a positive result for certification. By clarifying the hierarchy of proof systems further suggests the feasibility of using known formats such as Extended QU-Resolution or QRAT to certify QCDCL solvers.

The p-simulation is our main result, but we also make other observations from the specifics of the formalisation.

Proof Simulation via Round-based Strategy Extraction for QBF

Point cloud salient object detection has attracted the attention of researchers in recent years. Since existing works do not fully utilize the geometry context of 3D objects, blurry boundaries are generated when segmenting objects with complex backgrounds. In this paper, we propose a geometry-aware 3D salient object detection network that explicitly clusters points into superpoints to enhance the geometric boundaries of objects, thereby segmenting complete objects with clear boundaries. Specifically, we first propose a simple yet effective superpoint partition module to cluster points into superpoints. In order to improve the quality of superpoints, we present a point cloud class-agnostic loss to learn discriminative point features for clustering superpoints from the object. After obtaining superpoints, we then propose a geometry enhancement module that utilizes superpoint-point attention to aggregate geometric information into point features for predicting the salient map of the object with clear boundaries. Extensive experiments show that our method achieves new state-of-the-art performance on the PCSOD dataset.

Geometry-Aware 3D Salient Object Detection Network

This paper introduces a SAT-based technique that calculates a compact and complete symmetry-break for finite model finding. Classes of algebraic structures are typically described as first order logic formulas and the concrete algebras are models of these formulas. These models include an enormous number of isomorphic, i.e. symmetric, algebras.

Calculating a complete symmetry-break gives us a formula that has as models, exactly one canonical representative from each equivalence class of algebras. Thus, we enable answering questions about properties of the models so that computation and search are restricted to the set of canonical representations.

For instance, we can answer the question: How many non-isomorphic semigroups are there of size n? Questions of this type can be answered by counting the satisfying assignments of a SAT formula, which already filters out non-isomorphic models. Applying the introduced approach enables us to calculate numbers of algebraic structures not present in the literature and that go beyond the possibilities of pure enumeration approaches.

Complete Symmetry Breaking for Finite Models

This paper proposes a method for measuring conditional feature importance via generative modeling. In explainable artificial intelligence (XAI), conditional feature importance assesses the impact of a feature on a prediction model's performance given the information of other features. Model-agnostic post hoc methods to do so typically evaluate changes in the predictive performance under on-manifold feature value manipulations. Such procedures require creating feature values that respect conditional feature distributions, which can be challenging in practice. Recent advancements in generative modeling can facilitate this. For tabular data, which may consist of both categorical and continuous features, the adversarial random forest (ARF) stands out as a generative model that can generate on-manifold data points without requiring intensive tuning efforts or computational resources, making it a promising candidate model for subroutines in XAI methods. This paper proposes cARFi (conditional ARF feature importance), a method for measuring conditional feature importance through feature values sampled from ARF-estimated conditional distributions. cARFi requires only little tuning to yield robust importance scores that can flexibly adapt for conditional or marginal notions of feature importance, including straightforward extensions to condition on feature subsets and allows for inferring the significance of feature importances through statistical tests.

Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests

Reasoning about the causes behind observations is crucial to the formalization of rationality. While extensive research has been conducted on root cause analysis, most studies have predominantly focused on deterministic settings. In this paper, we investigate causation in more realistic nondeterministic domains, where the agent does not have any control on and may not know the choices that are made by the environment. We build on recent preliminary work on actual causation in the nondeterministic situation calculus to formalize more sophisticated forms of reasoning about actual causes in such domains. We investigate the notions of “Certainly Causes” and “Possibly Causes” that enable the representation of actual cause for agent actions in these domains. We then show how regression in the situation calculus can be extended to reason about various notions of actual causes.

Reasoning About Actual Causes in Nondeterministic Dynamic Domains

The development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies. Existing computational methods in antibody design often overlook crucial conformational changes that antigens undergo during the binding process, significantly impacting the reliability of the resulting antibodies. To bridge this gap, we introduce dyAb, a flexible framework that incorporates AlphaFold2-driven predictions to model pre-binding antigen structures and specifically addresses the dynamic nature of antigen conformation changes. Our dyAb model leverages a unique combination of coarse-grained interface alignment and fine-grained flow matching techniques to simulate the interaction dynamics and structural evolution of the antigen-antibody complex, providing a realistic representation of the binding process. Extensive experiments show that dyAb significantly outperforms existing models in antibody design involving changing antigen conformations. These results highlight dyAb's potential to streamline the design process for therapeutic antibodies, promising more efficient development cycles and improved outcomes in clinical applications.

dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen

Recently, a state-of-the-art series of algorithms—Goal-Conditioned Weighted Supervised Learning (GCWSL) methods—has been introduced to address the challenges inherent in offline goal-conditioned reinforcement learning (RL). GCWSL optimizes a lower bound on the goal-conditioned RL objective and has demonstrated exceptional performance across a range of goal-reaching tasks, offering a simple, effective, and stable solution. Nonetheless, researches has revealed a critical limitation in GCWSL: the absence of trajectory stitching capabilities. In response, goal data augmentation strategies have been proposed to enhance these methods. However, existing techniques often fail to effectively sample appropriate augmented goals for GCWSL. In this paper, we establish unified principles for goal data augmentation, emphasizing goal diversity, action optimality, and goal reachability. Building on these principles, we propose a Model-based Goal Data Augmentation (MGDA) approach, which leverages a dynamics model to sample more appropriate augmented goals. MGDA uniquely incorporates the local Lipschitz continuity assumption within the learned model to mitigate the effects of compounding errors. Empirical results demonstrate that MGDA significantly improves the performance of GCWSL methods on both state-based and vision-based maze datasets, outperforming previous goal data augmentation techniques in their ability to enhancing stitching capabilities.

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