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Reasoning-intensive retrieval aims to surface evidence that maximizes downstream reasoning utility rather than only topical similarity. This capability is increasingly vital for agentic retriever-in-the-loop systems such as Deep-Research. However, existing retriever evaluation benchmarks, exemplified by Bright, provide narrow gold sets and evaluate retrievers in isolation, which obscures their value inside realistic agent workflows. We introduce Bright-Pro, an evaluation framework that assesses the effectiveness of retrievers in agentic search systems. Bright-Pro covers a broad range of queries across diverse professional domains. For each query, we provide expert-annotated reasoning aspects, positive documents, a reference response, and evaluation rubrics, enabling fine-grained assessment of retriever performance. Beyond static evaluation, we further assess retrievers in the context of agentic search systems, measuring their practical utility when serving as core components within agentic workflows. Using Bright-Pro, we evaluate classical lexical, general-purpose, and reasoning-intensive retrievers, providing actionable insights for future retriever development.

ACL 2026

Rethinking Reasoning-Intensive Retrieval: Evaluating and Advancing Retrievers in Agentic Search Systems

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>
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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.

technical paper

The rapid advancement of multimodal large language models (MLLMs) offers new opportunities for complex scientific challenges, yet their application in earth science—especially at the graduate level—remains underexplored due to a lack of benchmarks reflecting the depth and complexity of geoscientific reasoning. Existing datasets often rely on synthetic data or simple figure-caption pairs, failing to capture the nuanced reasoning required for real-world applications. To address this, we introduce MSEarth, a multimodal scientific dataset and benchmark curated from high-quality, open-access publications. Covering the five major spheres of Earth science—atmosphere, cryosphere, hydrosphere, lithosphere, and biosphere—MSEarth features over 289K figures with refined captions enriched by contextual discussions and reasoning from the original papers. The benchmark supports tasks such as scientific figure captioning, multiple choice questions, and open-ended reasoning, providing a scalable, high-fidelity resource for developing and evaluating MLLMs in scientific reasoning.

MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs

More than half of the global population struggles to meet their civil justice needs due to limited legal resources. While Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, significant challenges remain even at the foundational step of legal issue identification. To investigate LLMs' capabilities in this task, we constructed a dataset from 769 real-world Malaysian Contract Act court cases, using GPT-4o to extract facts and generate candidate legal issues, annotated by senior legal experts, which reveals a critical limitation: while LLMs generate diverse issue candidates, their precision remains inadequate (GPT-4o achieves only 62%). To address this gap, we propose LePREC (Legal Professional-inspired Reasoning Elicitation and Classification), a neuro-symbolic framework combining neural generation with structured statistical reasoning. LePREC consists of: (1) a neuro component leverages LLMs to transform legal descriptions into question-answer pairs representing diverse analytical factors, and (2) a symbolic component applies sparse linear models over these discrete features, learning explicit algebraic weights that identify the most informative reasoning factors. Unlike end-to-end neural approaches, LePREC achieves interpretability through transparent feature weighting while maintaining data efficiency through correlation-based statistical classification. Experiments show a 30-40% improvement over advanced LLM baselines, including GPT-4o and Claude, confirming that correlation-based factor-issue analysis offers a more data-efficient solution for relevance decisions.

LePREC: Reasoning as Classification over Structured Factors for Assessing Relevance of Legal Issues

We study asymmetric vulnerability to embedding corruption in encoder-based language models, where uniform perturbations dis-proportionately degrade low-magnitude embeddings compared to high-magnitude ones. Since critical words concentrate in low-norm space, this asymmetry causes catastrophic degradation of grammatical and semantic structure even under moderate corruption. Existing robustness approaches either sacrifice clean performance or fail to generalize to higher corruption levels. To address this problem, we propose Robertha, an attention mechanism built on Modern Hopfield Networks, in which semantic patterns act as stable states (attractors) that pull corrupted embeddings toward correct representations. We introduce iterative refinement for differential recovery: heavily corrupted embeddings require multiple convergence steps, while lightly corrupted embeddings converge quickly. To strengthen this mechanism, we introduce Eigenspectrum Regularization (ESR), which enforces low-rank key structures by controlling eigenvalue entropy, creating strong, well separated attractors with wide recovery basins. Across 13 GLUE and SuperGLUE tasks, Robertha significantly outperforms existing robustness methods while maintaining competitive clean performance.

Robertha: Eigenspectrum Regularized Attention for Robust Natural Language Understanding

Incorrect information poses significant challenges by disrupting content veracity and integrity, yet most detection approaches struggle to jointly balance textual content verification with external knowledge modification under collapsed attention geometries. To address this issue, we propose a dual-head reasoning framework, BiMind, which disentangles content-internal reasoning from knowledge-augmented reasoning. In BiMind, we introduce three core innovations: (i) an attention geometry adapter that reshapes attention logits via token-conditioned offsets and mitigates attention collapse; (ii) a self-retrieval knowledge mechanism, which constructs an in-domain semantic memory through kNN retrieval and injects retrieved neighbors via feature-wise linear modulation; (iii) the uncertainty-aware fusion strategies, including entropy-gated fusion and a trainable agreement head, stabilized by a symmetric Kullback-Leibler agreement regularizer. To quantify the knowledge contributions, we define a novel metric, Value-of-eXperience (VoX), to measure instance-wise logit gains from knowledge-augmented reasoning. Experiment results on public datasets demonstrate that our BiMind model outperforms advanced detection approaches and provides interpretable diagnostics on when and why knowledge matters. Our BiMind model and the tested datasets are available at https://github.com/cvzh/BiMind.

BiMind: A Dual-Head Reasoning Model with Attention-Geometry Adapter for Incorrect Information Detection

The increasing use of large language models in mental health applications calls for principled evaluation frameworks that assess alignment with psychotherapeutic best practices beyond surface-level fluency. While recent systems exhibit conversational competence, they lack structured mechanisms to evaluate adherence to core therapeutic principles. In this paper, we study the problem of evaluating AI-generated therapist-like responses for clinically grounded appropriateness and effectiveness. We assess each therapists utterance along six therapeutic principles: non-judgmental acceptance, warmth, respect for autonomy, active listening, reflective understanding, and situational appropriateness using a fine-grained ordinal scale. We introduce FAITH-M, a benchmark annotated with expert-assigned ordinal ratings, and propose CARE, a multi-stage evaluation framework that integrates intra-dialogue context, contrastive exemplar retrieval, and knowledge-distilled chain-of-thought reasoning. Experiments show that CARE achieves an F-1 score of 63.34 versus the strong baseline Qwen3 F-1 score of 38.56 which is a 64.26% improvement, which also serves as its backbone, indicating that gains arise from structured reasoning and contextual modeling rather than backbone capacity alone. Expert assessment and external dataset evaluations further demonstrate robustness under domain shift, while highlighting challenges in modeling implicit clinical nuance. Overall, CARE provides a clinically grounded framework for evaluating therapeutic fidelity in AI mental health systems.

Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversation

The necessity of explicit linguistic representations has been increasingly questioned in the era of large language models (LLMs). In this work, we revisit this issue using Universal Dependencies (UD) as a case study, examining whether and in what ways this cross-lingual syntactic framework can still benefit contemporary LLMs. We focus on a cross-lingual adversarial paraphrase identification task that is designed to foreground the role of syntactic structure in semantic interpretation across languages. Within this setting, we systematically evaluate three strategies for integrating UD into LLMs: UD-Prompt, UD-Tuning, and UD-Attention. Our experiments show that, although the magnitude of gains depends on how UD-based structural priors interact with model behavior and cross-lingual variation, UD-augmented models consistently outperform their syntax-agnostic counterparts. Across strategies, we observe average accuracy improvements of 2.67%, 8.24%, and 2.53%, respectively. These findings demonstrate that linguistic knowledge remains informative for LLMs, offering practical value in cross-lingual settings where structural alignment is challenging.

On the Continued Value of Universal Dependencies in the Era of Large Language Models

In automated fact-checking (AFC), check-worthiness detection identifies claims requiring verification based on domain-specific criteria. On Wikipedia, this task instantiates as Citation Needed Detection (CND), which flags claims lacking supporting citations. However, existing research has largely overlooked lower-resource languages, and recent AFC pipelines rely on large language models (LLMs), which are inaccessible to low-resource organizations. We introduce MCN, a multilingual CND corpus spanning 18 languages across three resource levels, on which we conduct an extensive study of small decoder-based language models (SLMs). Our experiments show that SLMs fine-tuned with an encoder-style objective substantially outperform prompted LLMs across languages. We further present one of the first studies on cross-lingual CND, demonstrating that SLMs fine-tuned solely on English claims surpass LLMs, even with little to no target-language adaptation. Our findings have important implications for lower-resource Wikipedia communities and suggest that compact, task-specific models are preferable to LLMs for CND.

Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages

Evaluating LLM-generated business ideas is often harder to scale than generating them.
Unlike standard NLP benchmarks, business idea evaluation relies on multi-dimensional criteria such as feasibility, novelty, differentiation, user need, and market size, and expert judgments often disagree.
This paper studies a methodological question raised by such disagreement: should an automatic judge approximate an aggregate consensus, or model evaluators individually?
We introduce PBIG-DATA, a dataset of approximately 3,000 individual scores across 300 patent-grounded product ideas, provided by domain experts on six business-oriented dimensions:
specificity, technical validity, innovativeness, competitive advantage, need validity, and market size.
Analyses show substantial expert disagreement on fine-grained ordinal scores, while agreement is higher under coarse selection, suggesting structured heterogeneity rather than random noise.
We then compare three judge configurations: a rubric-only zero-shot judge, an aggregate judge conditioned on mixed evaluator histories, and a personalized judge conditioned on the target evaluator's scoring history.
Across dimensions and model sizes, personalized judges align more closely with the corresponding evaluator than aggregate judges, and evaluator agreement correlates with similarity of judge-generated reasoning only under personalized conditioning.
These results indicate that pooled labels can be a fragile target in pluralistic evaluation settings and motivate evaluator-conditioned judge designs for business idea assessment.

Aggregate vs. Personalized Judges in Business Idea Evaluation: Evidence from Expert Disagreement

We introduce Earth Virtual Expert (EVE), the first open-source, end-to-end initiative for developing and deploying domain-specialized LLMs for Earth Intelligence. At its core is EVE-Instruct, a domain-adapted 24B model built on Mistral Small 3.2 and optimized for reasoning and question answering. On newly constructed Earth Observation and Earth Sciences benchmarks, it outperforms comparable models while preserving general capabilities.
We release curated training corpora and the first systematic domain-specific evaluation benchmarks, covering MCQA, open-ended QA, and factuality. EVE further integrates RAG and a hallucination-detection pipeline into a production system deployed via API and GUI, supporting 350 pilot users. All models, datasets, and code are publicly available.

EVE: A Domain-Specific LLM Framework for Earth Intelligence

Robust explanations are increasingly required for user trust in enterprise NLP, yet pre-deployment validation is difficult in the common case of black-box deployment (API-only access) where representation-based explainers are infeasible and existing studies provide limited guidance on whether explanations remain stable under real user noise, especially when organizations migrate from encoder classifiers to decoder LLMs. To close this gap, we propose a unified black-box robustness evaluation framework for token-level explanations based on leave-one-out occlusion, and operationalize explanation robustness with top-token flip rate under realistic perturbations (swap, deletion, shuffling, and back-translation) at multiple severity levels. Using this protocol, we conduct a systematic cross-architecture comparison across three benchmark datasets and six models spanning encoder and decoder families (BERT, RoBERTa, Qwen 7B/14B, Llama 8B/70B; 64,800 cases). We find that decoder LLMs produce substantially more stable explanations than encoder baselines (73% lower flip rates on average), and that stability improves with model scale (44% gain from 7B to 70B). Finally, we relate robustness improvements to inference cost, yielding a practical cost–robustness tradeoff curve that supports model and explanation selection prior to deployment in compliance-sensitive applications.

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