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Large Language Models (LLMs) have achieved remarkable success on reasoning benchmarks through Reinforcement Learning with Verifiable Rewards (RLVR), excelling at tasks such as math, coding, logic and puzzles. However, existing benchmarks evaluate only correctness, overlooking optimality—the ability to find the best solutions under constraints. We propose \data, the first comprehensive framework for training and evaluating LLMs on NP-hard optimization problems through quality-aware RLVR. \data provides three key components: a scalable training infrastructure with instance generators, quality verifiers, and optimal baselines across 10 tasks; a rigorous benchmark with 1,000 instances evaluating both feasibility (Success Rate) and quality (Quality Ratio); and quality-aware rewards enabling continuous improvement beyond binary correctness. Training on Qwen2.5-7B-Instruct-1M with 15K examples achieves 93.1% SR and 46.6% QR, significantly outperforming GPT-4o (29.6% SR, 14.6% QR). Beyond optimization, training on \data transfers to diverse tasks: mathematics (+2.2%), logic (+1.2%), knowledge (+4.1%), and instruction-following (+6.1%). Our analysis reveals quality-aware rewards improve solutions by 28.8% over binary rewards, and task diversity drives generalization more than data quantity—offering insights into RLVR scaling for complex reasoning.

ACL 2026

 Forge: Quality-Aware Reinforcement Learning for NP-Hard Optimization in LLMs

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

poster

Code explanations are increasingly generated by large language models and used in software engineering workflows, making reliable evaluation essential. However, existing model-based and embedding-based methods often fail to distinguish correct explanations from partially or fully incorrect ones, and their similarity scores are poorly calibrated and do not reflect meaningful differences in explanation quality. To address this, we propose ODASim(Orderly, Dstinctive, and Absolute Similarity), a model-agnostic graded fine-tuning framework for embedding models that learns calibrated similarity representations between code and explanations. To support fine-grained supervision and evaluation, we also introduce ODA-X, a novel benchmark for code-to-explanation quality grading, comprising code–explanation pairs graded similarity labels derived from strategic perturbations of gold explanations. We apply our ODASim approach to multiple embedding models and evaluate it on two benchmarks: widely popular CodeXGLUE and our proposed benchmark ODA-X, spanning four programming languages - Python, Java, JavaScript, and Go. Results show that our method achieves up to 35% improvement in F1 score and 85% reduction in Expected Calibration Error (ECE), enabling reliable evaluation of code to explanation quality.

ODASim: Ordered, Distinctive and Absolute Semantic Similarity for Code Explanation Evaluation

In-context learning (ICL) with large language models (LLMs) has emerged as a powerful alternative to fine-tuning for Named Entity Recognition (NER), achieving strong performance with minimal annotation and no additional training. However, prior work has shown that despite their adaptability, LLMs still lag behind fully supervised models such as fine-tuned BERT in structured tasks like NER. While existing studies on ICL for NER have mainly explored few-shot settings, the potential of scaling to hundreds of demonstrations has not been thoroughly investigated. To address this gap, we conduct a comprehensive investigation of many-shot ICL for NER and further explore its effectiveness in annotating and refining data for low-resource NER tasks. Specifically, we evaluate various LLMs across multiple domains using hundreds of ICL examples and then assess the feasibility of using many-shot ICL as a data annotation framework. Our experiments demonstrate that: (1) scaling to hundreds of in-context examples enables LLMs to match or even surpass the performance of fully supervised BERT models; and (2) using about one hundred human-labeled examples as demonstrations, many-shot in-context annotation can generate high-quality labeled data, leading to approximately 10% absolute F1 improvement over existing state-of-the-art approaches when used to fine-tune BERT on low-resource NER.

Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition

We present a system that uses LLMs as a tool in the development of Constructed Languages— ConLangs, which we call IASC (Interactive Agentic System for ConLangs). The system is modular in that it creates each of the components—phonology, morphology and syntax, lexicon, orthography, and grammatical handbook, using module-specific sets of prompts. The approach is agentic in that various modules allow for refining the output given automatically-generated commentary on a previous step. Our main goals are twofold. First, we aim to provide tools that facilitate an engaging and enjoyable experience in creating artificially constructed languages. Second, the focus of this paper is on using our ConLang framework as a novel way to explore what LLMs ‘know’ about language—not what they know about any particular language or encyclopedic facts, but how much they know about and understand language and linguistic concepts. In the experiments, we particularly focus on the morphosyntax module and show that there is a fairly wide gulf in capabilities both among different LLMs and among different linguistic specifications, with it being notably easier for systems to deal with more typologically common patterns than rarer ones. All code is released: https://github.com/SakanaAI/IASC.

Creating ConLangs to Probe the Metalinguistic Grammatical Knowledge of LLMs

Large Multimodal Models (LMMs) achieve state-of-the-art performance in high-stakes domains like healthcare, yet their reasoning remains opaque. Attention- and saliency-based methods often fail to faithfully represent the model's decision process, particularly when integrating heterogeneous modalities. We introduce Tree-of-Evidence (ToE), an inference-time search algorithm that frames interpretability as a discrete optimization problem. Rather than relying on soft attention weights, ToE employs lightweight Evidence Bottlenecks that score coarse units of data (e.g., vital-sign windows, report chunks) and performs a beam search to identify the compact evidence set required to reproduce the model's prediction. We evaluate ToE across six tasks spanning three datasets and two domains, including clinical prediction on MIMIC-IV, cross-center validation on eICU, and non-clinical fault detection on LEMMA-RCA. ToE retains over 98\% of full-model AUROC with as few as five evidence units, achieves higher decision agreement and lower fidelity error than LIME, SHAP, saliency, and concept-bottleneck baselines under sparse budgets, and outperforms LLMs up to 70B parameters. ToE therefore provides a practical mechanism for auditing multimodal models.

Tree-of-Evidence: Efficient "System 2" Search for Faithful Multimodal Grounding

While LLMs increasingly assist individual users, there is a critical need for agents that can proactively manage complex, multi-party collaboration. However, the scarcity of systematic evaluation methods for these group dynamics limits the development of AI capable of effectively supporting teams Here, we present ProMediate, the first testbed for evaluating proactive AI mediator agents in complex, multi-topic, multi-party negotiations. ProMediate consists of two core components: (i) a simulation environment based on realistic negotiation cases with a plug-and-play proactive AI mediator, capable of flexibly deciding when and how to intervene; and (ii) a socio-cognitive evaluation framework with a new suite of metrics to measure consensus changes, intervention latency, mediator effectiveness, and intelligence. These components establish a systematic framework for assessing the capability of proactive AI agents in multi-party settings. Our results show that a socially intelligent mediator agent outperforms a generic baseline, via faster, better-targeted interventions. In the ProMediate-Hard setting, our social mediator increases consensus change by 3.6 percentage points compared to the generic baseline (10.65% vs 7.01%) while being 77% faster in response (15.98s vs. 3.71s). In conclusion, ProMediate provides a rigorous, theory-grounded testbed to advance the development of proactive, socially intelligent agents.

ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation

Larger language models become simultaneously better and worse at handling contextual information—better at ignoring false claims, worse at ignoring irrelevant tokens. We formalize this apparent paradox through the first scaling laws for contextual entrainment, the tendency of models to favor tokens that appeared in context regardless of relevance. Analyzing the Cerebras-GPT (111M–13B) and Pythia (14M–12B) model families, we find entrainment follows predictable power-law scaling, but with opposite trends depending on context type: semantic contexts show decreasing entrainment with scale, while non-semantic contexts show increasing entrainment. Concretely, the largest models are four times more resistant to counterfactual misinformation than the smallest, yet simultaneously twice as prone to copying arbitrary tokens. These diverging trends, which replicate across model families, suggest that semantic filtering and mechanical copying are functionally distinct behaviors that scale in opposition. These opposing trends suggest that scaling alone does not resolve context sensitivity—it reshapes it.

Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size

Indirect speech acts (ISAs) require pragmatic reasoning over context, as directive intent cannot be inferred from surface form alone. Prior text-based studies and existing multimodal benchmarks largely overlook this requirement, focusing instead on explicitly encoded context or perceptual recognition, and thus underexplore context-dependent pragmatic understanding—particularly in high-context languages such as Korean. We introduce READI, a multimodal benchmark for evaluating ISA understanding through integrated reasoning over visual context and dialogue. READI models graded indirectness grounded in pragmatic theory and formulates the task as vision-based pragmatic question answering (V-PQA), supporting cross-lingual evaluation in English and Korean. Experiments show that even state-of-the-art multimodal models struggle with visually grounded indirect speech acts, with performance declining as indirectness increases, underscoring the need for benchmarks that explicitly target contextual pragmatic reasoning.

 Read the Room, Read the Image: Understanding Indirect Speech Acts in Multimodal Visual Contexts

Existing information extraction (IE) tasks increasingly adopt in-context learning (ICL) with large language models. However, current approaches either show inconsistent performance across model scales or lack systematic optimization and generalizability. Building on this, we propose BCL-IE (Bayesian In-Context Learning Framework for Information Extraction), the first optimization framework that uses particle filtering with Bayesian updates to systematically refine label representations across IE tasks. Through four steps—initialization, observation, weight update, and resampling, BCL-IE generalizes to both sequence labeling and relation classification paradigms. Extensive experiments demonstrate substantial improvements over existing approaches (up to 30%), achieving prior performance while other methods either fail to generalize or show limited effectiveness.

 BCL: Bayesian In-Context Learning Framework for Information Extraction

The Uniform Information Density (UID) hypothesis proposes that effective communication is achieved by maintaining a stable flow of information. In this work, we revisit this principle in the context of Large Language Model (LLM) reasoning, asking whether step-level uniformity reflects reasoning quality. To this end, we introduce a novel framework to quantify uniformity of information flow at both local and global levels, using an entropy-based stepwise density metric. Across experiments on seven reasoning benchmarks, we see a counter-intuitive pattern: while high-quality reasoning exhibit smooth step-by-step transitions (local uniformity) and structured, non-uniform information flow at the trajectory level (global non-uniformity). The results demonstrate that these uniformities outperform alternative internal signals as predictors of reasoning quality, and such divergence with human communication is not a model deficiency, but a byproduct of distinct objectives between human communication and LLM reasoning.

 Revisiting the Uniform Information Density Hypothesis in LLM Reasoning

Enhancing the task-specific capabilities of Large Language Models (LLMs) primarily requires substantial instruction-tuning datasets. However, the sheer volume of such data imposes a considerable annotation cost, and a lack of optimization methods for tailoring LLMs to specific tasks persists. To address the above issues, we propose a Planning framework for constructing Extractive-based LLMs called PlanE, which includes data decomposition, instruction tuning, and prompt inference. Additionally, we introduce a Data-Tuning-Inference (DTI) planner, aimed at selecting the optimal base-LLM and its DTI combinations for specific datasets to improve construction efficiency. The experimental results demonstrate the effectiveness of our PlanE from two views: (1) across different datasets using the same base-LLM, and (2) on the same dataset using different base-LLMs. Furthermore, we validate the generalizability of the proposed DTI planner under different optimization objectives. The codes are publicly available at https://github.com/gugugu-469/PlanE.

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ODASim: Ordered, Distinctive and Absolute Semantic Similarity for Code Explanation Evaluation

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