United States

LLM-based agents depend on effective tool-use policies to solve complex tasks, yet optimizing these policies remains challenging due
to delayed supervision and the difficulty of
credit assignment in long-horizon trajectories.
Existing optimization approaches tend to be
either monolithic, which are prone to entangling behaviors, or single-aspect, which ignore
cross-module error propagation. To address
these limitations, we propose EVOTOOL, a self-
evolving framework that optimizes a modular
tool-use policy via a gradient-free evolutionary paradigm. EVOTOOL decomposes agent’s
tool-use policy into four modules, including
Planner, Selector, Caller, and Synthesizer, and
iteratively improves them through three mechanisms. Trajectory-Grounded Blame Attribution uses diagnostic traces to localize failures
to a specific module. Feedback-Guided Targeted Mutation then edits only that module
via natural-language critique. Diversity-Aware
Population Selection preserves complementary
candidates to ensure solution diversity. Across
four diverse benchmarks, EVOTOOL outperforms strong baselines by over 5 points on both
GPT-4.1 and Qwen3-8B, while achieving superior efficiency and transferability.

ACL 2026

EVOTOOL: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware Selection

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

You are required to register for this event. **Please register [here](https://2024.emnlp.org/registration/).** The EMNLP 2024 event page on Underline will be open to public one week prior to the event.

Please register!

EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

technical paper

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) 
<html><button style="display: inline-flex; align-items: center; justify-content: center; white-space: nowrap; border-radius: 9999px; font-weight: bold; background: #7c3aed; color: white; font-family: 'Space Grotesk', sans-serif; height: 40px; font-size: 16px; padding: 0 20px; border: none; cursor: pointer" onclick="window.open('https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing')">Conference Handbook</button></html>

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to ACL 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://2026.aclweb.org/registration/) first.

Registration Required

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

Reviewer assignment is increasingly critical yet challenging in the LLM era, where rapid topic shifts render many pre-2023 benchmarks outdated and where proxy signals poorly reflect true reviewer familiarity. We address this evaluation bottleneck by introducing LR-bench, a high-fidelity, up-to-date benchmark curated from 2024--2025 AI/NLP manuscripts with five-level self-assessed familiarity ratings collected via a large-scale email survey, yielding 1,055 expert-annotated paper--reviewer--score annotations. We further propose a reviewer-centric ranking framework that distills each reviewer’s recent publications into compact keyword-based profiles and fine-tunes an embedding model with weak preference supervision constructed from heuristic retrieval signals, enabling to match each manuscript against a reviewer profile directly. Across the LR-bench and the CMU gold-standard dataset, our approach consistently achieves state-of-the-art performance, outperforming strong embedding baselines by a clear margin. We release LR-bench at https://huggingface.co/datasets/Gnociew/LR-bench, and an github repository at https://github.com/Gnociew/RATE-Reviewer-Assignment.

RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems

Large language models (LLMs) have demonstrated increasingly strong reasoning capabilities, achieving remarkable progress in knowledge graph question answering (KGQA). However, a key challenge in such systems is non-deterministic reasoning, where the model indecisively activates multiple semantically related knowledge graph edges for a given query, frequently leading to incorrect answers. To address this issue, we propose Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA (DR2). DR2 identifies and localizes non-deterministic reasoning behaviors, uncovering the underlying semantic representation deficiencies in LLMs. Building on this diagnosis, we design abductive reasoning-based preference learning, which promotes fine-grained semantic discrimination and mitigates non-deterministic reasoning errors. Experimental results demonstrate that the proposed DR2 significantly outperforms several strong baselines, achieving state-of-the-art performance on the widely used WebQSP and CWQ benchmarks.

Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA

Large vision-language models (LVLMs) have made substantial advances in reasoning tasks at the Olympiad level. Nevertheless, current Olympiad-level multimodal reasoning benchmarks for these models often emphasize single-image analysis and fail to exploit contextual information across multiple images. We present OMIBench, a benchmark designed to evaluate Olympiad-level reasoning when the required evidence is distributed over multiple images. It contains problems from biology, chemistry, mathematics, and physics Olympiads, together with manually annotated rationales and evaluation protocols for both exact and semantic answer matching. Across extensive experiments on OMIBench, we observe meaningful performance gaps in existing models. Even the strongest LVLMs, such as Gemini-3-Pro, attain only about 50% on the benchmark. These results position OMIBench as a focused resources for studying and improving multi-image reasoning in LVLMs.

OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Models

As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult. While early efforts in MGT detection have focused on binary classification, the growing landscape and diversity of LLMs require a more fine-grained yet challenging authorship attribution (AA), i.e., being able to identify the precise generator (LLM or human) behind a text. However, AA remains nowadays confined to a monolingual setting, with English being the most investigated one, overlooking the multilingual nature and usage of modern LLMs. In this work, we introduce the problem of Multilingual Authorship Attribution, which involves attributing texts to human or multiple LLM generators across diverse languages. Focusing on 18 languages---covering multiple families and writing scripts---and 8 generators (7 LLMs and the human-authored class), we investigate the multilingual suitability of monolingual AA methods in terms of their cross-lingual transferability, and the impact of generators on attribution performance. Our results reveal that while certain monolingual AA methods can be adapted to multilingual settings, significant limitations and challenges remain, particularly in transferring across diverse language families, underscoring the complexity of multilingual AA and the need for more robust approaches to better match real-world scenarios.

Authorship Attribution in Multilingual Machine-Generated Texts

Multi-task learning (MTL) enables joint learning over multiple tasks based on shared representations, but suffers from task interference issue during optimization. Existing works mainly focus on task balancing or probabilistic modeling but fail to address the issue since they struggle to learn sufficient representations for all target tasks. To address this, we propose a multi-task representation alignment (MTRA) framework to achieve task-specific alignment and self-alignment on the shared representations from a mutual information perspective. MTRA ensures that the learned representations contain task-relevant features while mitigating the negative effects of task-irrelevant features. First, we design a task-specific alignment objective to align the shared representations and task-specific representations with the expected targets of all tasks via information maximization. Besides, we design a self-alignment objective to eliminate task-irrelevant features via conditional information minimization. Experiments on two multi-task language benchmarks show that MTRA outperforms 13 representative MTL methods under the same settings, particularly under label-noisy and data-constrained conditions. Further analysis shows that the learned shared representations exhibit sufficient task informativeness and superior alignment properties.

Multi-Task Representation Alignment on Language Understanding: A Mutual Information Perspective

Extracting structured information from visual documents (Visual Information Extraction, VIE) is a cornerstone of business automation. While recent Multimodal Large Language Models (MLLMs) have shown promising capabilities, existing benchmarks suffer from critical limitations in scale and realism, lack semantic granularity, and fail to cover diverse document types. To bridge this gap, we introduce ReceiptBench, a large-scale, human-annotated benchmark consisting of 10k diverse receipts, organizing information extraction into four hierarchical sub-tasks: (1) Basic Perception for raw text spotting, (2) Format Normalization for strictly following standardization instructions, (3) Semantic Reasoning for inferring implicit attributes from context, and (4) Structure Parsing for handling nested line items. Furthermore, we propose a two-stage training framework incorporating Metric-Aware Group Relative Policy Optimization (GRPO), which translates rigorous evaluation constraints into reinforcement learning signals to enhance structural consistency. Extensive experiments demonstrate that our method yields state-of-the-art performance, surpassing leading proprietary models on complex reasoning tasks. We release our datasets and code at https://github.com/wwwT0ri/ReceiptBench.

From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding

Hallucinated object mentions remain a persistent failure mode of vision–language models (VLMs) across generation tasks such as image captioning and visual question answering: outputs may be fluent yet include entities not supported by visual evidence. Existing mitigation approaches often reduce hallucinations at the cost of degraded generation quality or require expensive retraining and task-specific supervision. We introduce CEBC, a lightweight, training-free framework for low-hallucination vision–language generation based on conformal evidence-bounded minimal editing. CEBC first produces a strong base output (via greedy decoding or best-of-K sampling), then applies an evidence-bounded editing step that minimally revises or suppresses unsupported object mentions using constraints derived from an external visual detector. Crucially, the evidence threshold is conformally calibrated on a small held-out set via quantiles of detector confidence scores, enabling explicit and controllable hallucination risk at test time.

To balance factuality and informativeness, we further introduce a risk-first, quality-aware selection rule that prioritizes evidence-consistent generations while regularizing unnecessary length or lexical drift. Extensive experiments on MS-COCO and GQA for image captioning, and POPE for VQA evaluation across multiple VLMs demonstrate that CEBC consistently reduces hallucination rates(CHAIR_S, CHAIR_I, POPE) while maintaining or improving standard generation quality metrics (CIDEr, BLEU, CLIPScore). CEBC establishes a stronger factuality–quality Pareto frontier without any additional model training or access to paired supervision beyond an off-the-shelf detector.

CEBC: Conformal Evidence-Bounded Control for Low-Hallucination Vision–Language Generation

Speculative decoding has emerged as a promising paradigm for accelerating large language model inference by leveraging a lightweight draft model to generate multiple candidate tokens. However, existing methods often incur substantial training overhead to mitigate information misalignment between autoregressive draft model training and decoding. To address this challenge, we propose EDSD, an Entropy-Driven Speculative Decoding framework that uses entropy as a unified, interpretable signal for both draft model training and architectural design. EDSD drives the draft model to progressively align with the target model in an easy-to-hard manner while establishing token-level alignment as a dominant design principle. Extensive experiments on seven LLMs demonstrate that EDSD improves training efficiency by 24.8%, increases the average acceptance length by 4.0%, and achieves a 4.1% speedup compared to state-of-the-art methods. Furthermore, EDSD improves robustness to system prompt variations by more than 5x. Our findings establish entropy-driven alignment as an effective and principled foundation for efficient speculative decoding.

EDSD: Entropy-Driven Design for Faster Speculative Decoding

With the growing popularity of virtual character platforms like Character.AI, users are increasingly turning to role-playing agents for emotional support in daily life. Yet existing research mainly focuses on character consistency in fictional or game-based scenarios, overlooking user-centered interactions such as companionship and psychological support. To bridge this gap, we propose Emotionally Supportive Role-Playing (ESRP), a framework designed to align role-playing with real-world user scenarios and emotional needs. We focus on typical users of these platforms, i.e., anime enthusiasts—including students, office workers, freelancers, and self-employed individuals—and design scenario-based questions that reflect their everyday struggles such as work stress and social loneliness. Through a two-round data collection involving 40 anime fans and 10 Large Language Models (LLMs), we build ChatAnime: the first ESRP dataset with 2,400 human-written and 24,000 LLM-generated responses, supported by over 132,000 fine-grained human annotations. We also provide the ESRP evaluation framework featuring 9 fine-grained metrics across three dimensions: basic dialogue, role-playing and emotional support, along with an overall metric for diversity. Experimental results under our evaluation setting show that top-performing LLMs surpass anime fans in role-playing and emotional support, while humans still lead in diversity.

ChatAnime: Towards User-Centered Emotional Support in LLM-based Virtual Character Chat

Unsupervised representation learning using masked language modeling on the language of life has transformed protein research, enabling the analysis of a protein universe that is expanding at an exponential pace. However, most current models rely solely on sequence data, overlooking decades of expert-curated biological knowledge stored in natural language. While recent multimodal and knowledge-graph-based approaches attempt to bridge this gap, they often rely on shallow functional labels that lack the contextual depth of full textual narratives. We present Protein-STORY, a general pipeline that synthesizes protein embeddings from diverse, multi-source text descriptions. At the core of our approach is a novel network architecture designed for the semantic compression of multi document embeddings, which integrates high-fidelity functional and structural insights into a unified representation. Our experiments demonstrate that Protein-STORY produces biologically meaningful embeddings ($r \approx 0.75$) that outperform existing models on diverse downstream tasks (+2 pts F1 in function prediction). Furthermore, by projecting the story of a protein into a natural language semantic space, our model enables effective zero-shot text-prompted protein search.

Downloads

Next from ACL 2026

RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems

Similar lecture

Small LLMs Are Weak Tool Learners: A Multi-LLM Agent

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ACL 2026

RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems

Similar lecture

Small LLMs Are Weak Tool Learners: A Multi-LLM Agent

Downloads