Computation and Language
Community-Specific Slang and Entity Detection via Semantic Shift in Fine-Tuned Language Models
Julia Kruk, Sanchita Porwal, Amitrajit Bhattacharjee et al. (+1 more)
We propose an unsupervised method of resolving slang, unique entities, and folklore from online communities by isolating words in the lexicon that have the highest magnitude of semantic shift. Semantic shift is defined as the
evolution of a word's encoded representation as a result of fine-tuning a pretrained Large
Language Model (LLM) on a community-specific text corpus. This value is inversely proportional to the cosine similarity between the base model's encoded representation of a word, and a fine-tuned model's encoded representation. We fine-tune the DistilRoBERTa model on text corpora collected from 3 Reddit subreddits (r/Technology, r/Gaming, r/WorldofWarcraft), model a distribution of cosine similarity over the lexicon, and show that one can successfully resolve words that have unique significance to the community by pulling data in the bottom 10-percentile. In contrast, we show that data in the top 10-percentile consist of words that carry relatively universal semantics.
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All authors Julia Kruk, Sanchita Porwal, Amitrajit Bhattacharjee, Mansi Phute
Computation and Language
Sanchita Porwal, Sai Prasath S, Xingjian Bi et al. (+1 more)
This study investigates the phenomenon of
hallucinations in domain-adapted Large
Language Models (LLMs), focusing on the fine-tuning of the Llama-2 model with the Lamini
dataset.
Hallucinations, or the generation of nonsensical or unfaithful content by LLMs, pose a significant challenge, especially when these models are fine-tuned with domain-specific data. Our methodology involves a series of experiments testing memorization, recall, and
reasoning capabilities of the fine-tuned LLM, comparing its performance on novel question-answer pairs and domain-specific information. We found that while the model shows proficiency in tasks similar to its training data, its capability to accurately reason about and recall new domain-specific information remains limited, leading to instances of
hallucination. The model demonstrates a tendency to provide correct answers with extra information, suggesting an inclination toward over-generation. These results suggest important limitations of fine-tuning-only approaches for mitigating
hallucinations when adapting LLMs to specialized domains and underscore the need for more robust methods in adapting LLMs to specialized domains. The study also provides insights into the varying performance of LLMs on different types of information, revealing a comparative weakness in handling domain-specific queries.
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All authors Sanchita Porwal, Sai Prasath S, Xingjian Bi, Madelyn Scandlen
Computation and Language
TinyJudge: Unverifiable Constraint Alignment via Lightweight Specialist Ensembles
Yirong Zeng, Yufei Liu, Xiao Ding et al. (+9 more)
Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints, ranging from verifiable ones (e.g., output length) to unverifiable ones (e.g., tone). Reinforcement learning with verifiable rewards has emerged as a paradigm for IF tasks, leveraging LLM-as-a-judge to assess unverifiable constraints. However, we empirically find that this approach remains a significant bottleneck, suffering from severe reward hacking and higher computational overhead. In this work, we first analyze the generalization capabilities of unverifiable constraints and discover that specific constraints exhibit distinct, high-generalization patterns. Motivated by this, we propose TinyJudge, a framework that employs an ensemble of specialized tiny
language models ($\sim0.6B$) to provide rewards for soft constraints. By distilling expertise from frontier models into these tiny models, it achieves high-precision, lightweight evaluation. Extensive evaluations across five
benchmarks demonstrate that TinyJudge outperforms the baselines by $\sim10\%$ in average performance and $12\%$ in reward precision. Crucially, it also achieves a $3\times$ speedup in total training time. Our work provides a scalable and robust path for aligning LLMs with unverifiable human instructions.
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All authors Yirong Zeng, Yufei Liu, Xiao Ding, Yutai Hou, Yuxian Wang, Wu Ning, Haonan Song, Dandan Tu, Qixun Zhang, Yuxiang He, Bibo Cai, Ting Liu
Computation and Language
Bidirectional Small-Granularity Search between Code and Text
Marco A. Valenzuela-Escárcega, Enrique Noriega-Atala, Gus Hahn-Powell et al. (+2 more)
We introduce the novel task of bidirectional small-granularity search between code and text, where the queries are small snippets of text or code and the results are also small fragments of the opposite modality, i.e., code or text. This task establishes direct links between text in scientific publications and corresponding code segments, in support of better and faster understanding of scientific methods. We introduce a large
dataset for the proposed task that includes a training partition with textual descriptions of code generated automatically using GPT-4, and three testing partitions, one in-domain and two out-of-domain (OOD) that contain manually-annotated data as well as material from other domains. We also propose a modular approach to address this task. Our approach shares an encoder across four different subtasks that learn start/end of answer spans in both directions. We show that our method achieves good results in-domain, and encouraging results OOD. This suggests that addressing this task with automatically-generated data is possible, but there is exciting future work to be done.
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All authors Marco A. Valenzuela-Escárcega, Enrique Noriega-Atala, Gus Hahn-Powell, Clayton T. Morrison, Mihai Surdeanu
Computation and Language
The Granularity Gap: A Multi-Dimensional Cross-Generational Audit of Sycophancy in Gemini Models
Patrick Keough
Pass/fail safety evaluation reports whether a model refused. It does not report how far a model went to please the user, and we show these are close to different measurements. We audited sycophancy across three Gemini generations, scoring N=8,830 responses from 8 model variants on 350
adversarial prompts in 7 categories under 3 guardrail conditions, on continuous 1-5 scales for sycophancy, truthfulness and refusal. The judge's own refuse-or-comply verdict explains 29% of the variance in its own sycophancy scores. We term the remainder the Granularity Gap, and it does not close under recalibration: the cut point already in use is the best available on the refusal axis, and no function of that axis explains more than 35%. Reading what four judges wrote while scoring shows why. On a quarter to a third of votes they record that the
prompt asked for nothing harmful, almost never in the two categories that solicit a harmful act and up to half the time in the five that do not. A verdict built on refusal has nothing to grade there. Three findings follow. Sycophancy co-occurs with degraded judged truthfulness (rho=0.40), a coupling that strengthens across generations. Capability moved and resistance did not: Gemini 2.0 Flash scores 1.43 and Gemini 3.0 Pro Preview 1.42, with a sharp Gen 2.5 regression between them. And a single direct instruction outperforms an elaborate
reasoning protocol in seven of eight variants, cutting mean severity in the most vulnerable category by 60.9%. We evaluate one judge's verdict, not a deployed safety classifier. We release the
prompt set, the rubric, and 10,792 per-vote judge scores with their written
reasoning.
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Computation and Language · Cross-listed from cs.LG
Internalizing Outcome Supervision into Process Supervision: A New Paradigm for Reinforcement Learning for Reasoning
Fei Ding, Yongkang Zhang, Runhao Liu et al. (+4 more)
The central challenge of reinforcement learning for
reasoning lies not only in the sparsity of outcome-level supervision, but more fundamentally in how to transform feedback provided only at the end of a sequence into fine-grained learning signals that can guide intermediate
reasoning steps. Existing approaches either rely on outcome-level rewards for sequence-level optimization, which makes precise credit assignment difficult, or depend on externally constructed process supervision, which is costly and difficult to scale sustainably. To address this, we propose a new perspective: reinforcement learning for
reasoning can be understood as the problem of internalizing outcome supervision into process supervision. From this perspective, we introduce a supervision-internalization method for reinforcement learning for
reasoning, enabling the model to automatically extract process-level learning signals through identifying, correcting, and reusing failed
reasoning trajectories, thereby achieving finer-grained policy optimization under outcome-only supervision. We further abstract this idea into a new training paradigm, in which the model continually generates and refines its own internal process supervision during reinforcement learning, opening a new path for fine-grained credit assignment in reinforcement learning for
reasoning that differs from externally provided process supervision.
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All authors Fei Ding, Yongkang Zhang, Runhao Liu, Yuhao Liao, Zijian Zeng, Sibo wang, Huiming Yang
Computation and Language
Peerispect: Claim Verification in Scientific Peer Reviews
Ali Ghorbanpour, Soroush Sadeghian, Alireza Daghighfarsoodeh et al. (+4 more)
Peer review is central to scientific publishing, yet reviewers frequently include claims that are subjective, rhetorical, or misaligned with the submitted work. Assessing whether review statements are factual and verifiable is crucial for
fairness and accountability. At the scale of modern conferences and journals, manually inspecting the grounding of such claims is infeasible. We present Peerispect, an interactive system that operationalizes claim-level verification in peer reviews by extracting check-worthy claims from peer reviews, retrieving relevant evidence from the manuscript, and verifying the claims through natural
language inference. Results are presented through a visual interface that highlights evidence directly in the paper, enabling rapid inspection and interpretation. Peerispect is designed as a modular Information
Retrieval (IR) pipeline, supporting alternative retrievers, rerankers, and verifiers, and is intended for use by reviewers, authors, and program committees. We demonstrate Peerispect through a live, publicly available demo (https://app.reviewer.ly/app/peerispect) and API services (https://github.com/Reviewerly-Inc/Peerispect), accompanied by a
video tutorial (https://www.youtube.com/watch?v=pc9RkvkUh14).
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All authors Ali Ghorbanpour, Soroush Sadeghian, Alireza Daghighfarsoodeh, Sajad Ebrahimi, Negar Arabzadeh, Seyed Mohammad Hosseini, Ebrahim Bagheri
Computation and Language · Cross-listed from cs.LG
ATLAS: Constitution-Conditioned Latent Geometry and Redistribution Across Language Models and Neural Perturbation Data
Gareth Seneque, Lap-Hang Ho, Nafise Erfanian Saeedi et al. (+2 more)
Constitution-conditioned post-training can be analysed as a structured perturbation of a model's learned representational geometry. We introduce ATLAS, a geometry-first program that traces constitution-induced hidden-state structure across charts, models, and substrates. Instead of treating the relevant unit as a single behaviour,
neuron, vector, or patch, ATLAS tests a local chart whose tangent structure, occupancy distribution, and behavioural coupling can be measured under system change. On Gemma, the anchored source-local chart captures 310 / 320 reviewed source rows and all 84 / 84 reviewed score-flip rows, but compact exact-patch sufficiency does not close, so the exportable unit is the broader source-defined family. Freezing that family, we re-identify a target-local realisation in an unadapted Phi model, where the fully adjudicated confirmatory contrast separates with AUC 0.984 and mean gap 5.50. In held-out ALM8 mouse frontal-cortex perturbation data, the same source-defined family receives support across 5/5 folds, with mean held-out AUC 0.72 and mean fold gap 4.50. A multiple-choice analysis provides the main boundary: nearby target-local signals can appear without source-faithful closure. The resulting correspondence is not coordinate identity, site identity, or a target-side mediation
theorem. It is
geometric recurrence under redistribution: written constitutions can induce recoverable latent geometry whose organisation remains detectable across model and substrate changes while its local coordinates, occupancy, and behavioural expression shift.
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All authors Gareth Seneque, Lap-Hang Ho, Nafise Erfanian Saeedi, Jeffrey Molendijk, Tim Elson
Computation and Language
Semantic Density Effect (SDE): Maximizing Information Per Token Improves LLM Accuracy
Amr Ahmed
We introduce the Semantic Density Effect (SDE): the empirical finding that
prompts carrying higher semantic information per
token consistently produce more accurate, focused, and less hallucinated outputs across all major LLM families. SDE is defined as the ratio of semantically loaded
tokens to total
prompt tokens, adjusted for redundancy and concreteness. Unlike prior
prompt optimization techniques that add
tokens (Chain of Thought), duplicate the
prompt (
Prompt Repetition), or reorder components (Instruction Placement Effect), SDE improves performance by removing or replacing low-information
tokens while preserving or sharpening the semantic signal. Evaluated across five frontier models and seven
benchmarks, ultra-dense
prompts (SDE > 0.80) outperform diluted counterparts by an average of +8.4 percentage points with 0 additional
tokens and 0 latency overhead. Combined with Instruction Placement Effect (IPE), the gain reaches +11.7 percentage points
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Computation and Language · Cross-listed from cs.MA
Towards Self-Improving Error Diagnosis in Multi-Agent Systems
Jiazheng Li, Emine Yilmaz, Bei Chen et al. (+1 more)
Large
Language Model (LLM)-based Multi-
Agent Systems (MAS) enable complex problem-solving but introduce significant debugging challenges, characterized by long interaction traces, inter-
agent dependencies, and delayed error manifestation. Existing diagnostic approaches often rely on expensive expert annotation or ”LLM-as-a-judge” paradigms, which struggle to pinpoint decisive error steps within extended contexts. In this paper, we introduce ErrorProbe, a self-improving framework for semantic failure attribution that identifies responsible
agents and the originating error step. The framework operates via a three-stage pipeline: (1) operationalizing the MAS failure taxonomy to detect local anomalies, (2) performing symptom-driven backward tracing to prune irrelevant context, and (3) employing a specialized multi-
agent team (Strategist, Investigator, Arbiter) to validate error hypotheses through tool-grounded execution. Crucially, ErrorProbe maintains a verified episodic
memory that updates only when error patterns are confirmed by executable evidence, without the need for annotation. Experiments across the TracerTraj and Who&When
benchmarks demonstrate that ErrorProbe significantly outperforms baselines, particularly in step-level localization, while the verified
memory enables robust cross-domain transfer without retraining.
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All authors Jiazheng Li, Emine Yilmaz, Bei Chen, Dieu-Thu Le
Computation and Language · Cross-listed from cs.SD
Video-Robin: Autoregressive Diffusion Planning for Intent-Grounded Video-to-Music Generation
Vaibhavi Lokegaonkar, Aryan Vijay Bhosale, Vishnu Raj et al. (+5 more)
Video-to-music (V2M) is the fundamental task of creating background music for an input
video. Recent V2M models achieve audiovisual
alignment by typically relying on visual conditioning alone and provide limited semantic and stylistic controllability to the end user. In this paper, we present
Video-Robin, a novel text-conditioned
video-to-music generation model that enables fast, high-quality, semantically aligned music generation for
video content. To balance musical fidelity and semantic understanding,
Video-Robin integrates autoregressive planning with
diffusion-based synthesis. Specifically, an autoregressive module models global structure by semantically aligning visual and textual inputs to produce high-level music latents. These latents are subsequently refined into coherent, high-fidelity music using local
Diffusion Transformers. By factoring semantically driven planning into
diffusion-based synthesis,
Video-Robin enables fine-grained creator control without sacrificing audio realism. Our proposed model outperforms baselines that solely accept
video input and additional feature conditioned baselines on both in-distribution and out-of-distribution
benchmarks with a 2.21x speed in inference compared to SOTA. We will open-source everything upon paper acceptance.
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All authors Vaibhavi Lokegaonkar, Aryan Vijay Bhosale, Vishnu Raj, Gouthaman KV, Ramani Duraiswami, Lie Lu, Sreyan Ghosh, Dinesh Manocha
Computation and Language
Measuring Distribution Shift in User Prompts and Its Effects on LLM Performance
Parker Seegmiller and Sarah Masud Preum
LLMs are increasingly deployed in dynamic, real-world settings, where the distribution of user
prompts can shift substantially over time as new tasks,
prompts, and users are introduced to a deployed model. Such natural
prompt distribution shift poses a major challenge to LLM reliability, particularly for specialized models designed for narrow domains or user populations. Despite
attention to out-of-distribution robustness, there is very limited exploration of measuring natural
prompt distribution shift in prior work, and its impact on deployed LLMs remains poorly understood. We introduce the LLM Evaluation under Natural
prompt Shift (LENS) framework: a data-centric approach for quantifying natural
prompt distribution shift and evaluating its effect on the performance of deployed LLMs. We perform a large-scale evaluation using 192 real-world post-deployment
prompt shift settings over time, user group, and geographic axes, training a total of 81 models on 4.68M training
prompts, and evaluating on 57.6k
prompts. We find that even moderate shifts in user
prompt behavior correspond with large performance drops (73% average loss) in deployed LLMs. This performance degradation is particularly prevalent when users from different latent groups and geographic regions interact with models and is correlated with natural
prompt distribution shift over time. We systematically characterize how LLM instruction following ability degrades over time and between user groups. Our findings highlight the critical need for data-driven monitoring to ensure LLM performance remains stable across diverse and evolving user populations.
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Computation and Language
ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts
Olubusayo Olabisi, Ekata Mitra and Ameeta Agrawal
Summarizing deeply nested discussion threads requires handling interleaved replies, quotes, and overlapping topics, which standard LLM summarizers struggle to capture reliably. We introduce ThreadSumm, a multi-stage LLM framework that treats thread summarization as a hierarchical
reasoning problem over explicit aspect and content unit representations. Our method first performs content planning via LLM-based extraction of discourse aspects and Atomic Content Units, then applies sentence ordering to construct thread-aware sequences that surface multiple viewpoints rather than a single linear strand. On top of these interpretable units, ThreadSumm employs a Tree of Thoughts search that generates and scores multiple paragraph candidates, jointly optimizing coherence and coverage within a unified search space. With this multi-proposal and iterative refinement design, we show improved performance in generating logically structured summaries compared to existing baselines, while achieving higher aspect retention and opinion coverage in nested discussions.
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Computation and Language
Copy First, Translate Later: Interpreting Translation Dynamics in Multilingual Pretraining
Felicia Körner, Maria Matveev, Florian Eichin et al. (+3 more)
Large
language models exhibit impressive cross-lingual capabilities. However, prior work analyzes this phenomenon through isolated factors and at sparse points during training, limiting our understanding of how cross-lingual generalization emerges–particularly in the early phases of learning. To study the early trajectory of linguistic and translation capabilities, we pretrain a
multilingual 1.7B model on nine diverse
languages, capturing checkpoints at a much finer granularity. We use word-level translation as a testbed, introducing a novel
dataset to trace how translation develops over training through behavioral analyses, model-component analysis, and parameter-based ablations. We find that the model quickly acquires basic linguistic capabilities in parallel with
token-level copying, while translation develops in two distinct phases: an initial phase dominated by copying and surface-level similarities, and a second phase in which more generalizing translation mechanisms are developed while copying is refined. Together, these findings provide a fine-grained view of how cross-lingual generalization develops during
multilingual pretraining.
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All authors Felicia Körner, Maria Matveev, Florian Eichin, Gitta Kutyniok, Barbara Plank, Michael A. Hedderich
Computation and Language
Does Welsh media need a review? Detecting bias in Nation.Cymru's political reporting
Cai Parry-Jones
Wales' political landscape has been marked by growing accusations of bias in Welsh media. This paper takes the first computational step toward testing those claims by examining Nation.Cymru, a prominent Welsh political news outlet. I use a two-stage natural
language processing (NLP) pipeline: (1) a robustly optimized BERT approach (RoBERTa) bias detector for efficient bias discovery and (2) a large
language model (LLM) for target-attributed sentiment classification of bias labels from (1). A primary analysis of 15,583 party mentions across 2022-2026 news articles finds that Reform UK attracts biased framing at twice the rate of Plaid Cymru and over three times as negative in mean sentiment (p<0.001). A secondary analysis across four parties across both news and opinion articles shows that Plaid Cymru is the outlier, receiving markedly more favourable framing than any other party. These findings provide evidence of measurable differential framing in a single Welsh political media outlet, supporting calls for a broader review of Welsh media coverage. Furthermore, the two-stage pipeline offers a low-cost, replicable framework for extending this analysis to other Welsh outlets, as well as media ecosystems outside of Wales.
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Computation and Language · Cross-listed from cs.AI
Toward Reusability of AI Models Using Dynamic Updates of AI Documentation
Peter Bajcsy and Walid Keyrouz
This work addresses the challenge of disseminating reusable artificial intelligence (AI) models accompanied by AI documentation (a.k.a., AI model cards). The work is motivated by the large number of trained AI models that are not reusable due to the lack of (a) AI documentation and (b) the temporal lag between rapidly changing requirements on AI model reusability and those specified in various AI model cards. Our objectives are to shorten the lag time in updating AI model card templates and align AI documentation more closely with current AI best practices. Our approach introduces a methodology for delivering agile, data-driven, and community-based AI model cards. We use the Hugging Face (HF) repository of AI models, populated by a subset of the AI research and development community, and the AI consortium-based Zero Draft (ZD) templates for the AI documentation of AI
datasets and AI models, as our test
datasets. We also address questions about the value of AI documentation for AI reusability. Our work quantifies the correlations between AI model downloads/likes (i.e., AI model reuse metrics) from the HF repository and their documentation
alignment with the ZD documentation templates using tables of contents and word statistics (i.e., AI documentation quality metrics). Furthermore, our work develops the infrastructure to regularly compare AI documentation templates against community-standard practices derived from millions of uploaded AI models in the Hugging Face repository. The impact of our work lies in introducing a methodology for delivering agile, data-driven, and community-based standards for documenting AI models and improving AI model reuse.
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Computation and Language · Cross-listed from cs.AI
Characterizing Model-Native Skills
Feiyang Kang, Mahavir Dabas, Myeongseob Ko et al. (+1 more)
Skills are a natural unit for describing what a
language model can do and how its behavior can be changed. However, existing characterizations rely on human-written taxonomies, textual descriptions, or manual profiling pipelines–all external hypotheses about what matters that need not align with the model's internal representations. We argue that when the goal is to intervene on model behavior, skill characterization should be *model-native*: grounded in the model's own representations rather than imposed through external ontologies. We instantiate this view by recovering a compact orthogonal basis from sequence-level activations. The resulting basis is semantically interpretable but need not correspond to any predefined human ontology; instead, it captures axes of behavioral variation that the model itself organizes around. We validate this characterization on
reasoning post-training, using the recovered basis for both SFT data selection and inference-time steering. We develop lightweight proxy interventions to identify which directions are most useful for a given model. Across Llama3-8B and Qwen2.5-3B, selecting data along those directions improves Pass@1 by up to 20% on MATH and 41% on AMC, outperforming data selection based on human-characterized skills. Because the basis lives in activation space, the same directions also serve as steering vectors at inference time, improving Pass@8 by up to 4.8% on MATH–an intervention that human-characterized skills cannot support. We further validate the characterization on safety
alignment, where selecting
adversarial training data for model-native skill coverage rather than textual diversity yields more sample-efficient learning. These results suggest that recovering skills from the model's own representations, rather than imposing them externally, provides a more effective foundation for intervening on model behavior. Codes are open-sourced.
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All authors Feiyang Kang, Mahavir Dabas, Myeongseob Ko, Ruoxi Jia
Computation and Language
Agents Explore but Agents Ignore: LLMs Lack Environmental Curiosity
Leon Engländer, Sophia Althammer, Ahmet Üstün et al. (+2 more)
LLM-based
agents are assumed to integrate environmental observations into their
reasoning: discovering highly relevant but unexpected information should naturally lead to a model exploiting its own discoveries. We show that this assumption is false for current LLM-based
agents, which struggle to reflect or react to unexpected information. Across three
benchmarks (Terminal-Bench, SWE-Bench, AppWorld), we inject complete task solutions into the
agent environments to deliberately expose a task's solution to a model. While
agents discover these solutions on Terminal-Bench in 79-81% of runs, they interact, or exploit, them in only 37-50% of cases. This gap is starkest in AppWorld:
agents see documentation stating that a command "returns the complete solution to this task" in over 90% of attempts but exploit this in fewer than 7% of trials. We show that
agents lack what we call environmental curiosity: the capability to recognize and investigate unexpected but relevant observations in response to environmental stimuli. We identify three main factors influencing environmental curiosity: available tools in the
agent scaffold, test-time compute, and training data distribution. Our findings identify configurations that maximize curiosity also achieve the best performance on the unmodified
benchmarks. Yet even jointly optimized
agents still ignore discovered solutions in the majority of trials: current
agents use the environment to fetch expected information, but not to revise their strategy or maximally exploit useful stimuli.
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All authors Leon Engländer, Sophia Althammer, Ahmet Üstün, Matthias Gallé, Tom Sherborne
Computation and Language
Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation
Elaf Alhazmi, Quan Z. Sheng and Wei Emma Zhang
Distractor generation (DG) remains a labor-intensive task that still significantly depends on domain experts. The task focuses on generating plausible yet incorrect options, known as distractors, for multiple-choice questions. A reliable distractor must be contextually relevant to the question and able to mislead examinees through implicit
reasoning when identifying the correct answer. While a recent method integrates fine-tuning pre-trained encoder-decoder models with contrastive learning to generate semantically relevant distractors for a given question-answer, it often fails to capture the underlying
reasoning process that experts utilize when selecting distractors in
benchmarks. In this paper, we explore large
language models (LLMs)
reasoning for DG through in-context learning with unsupervised semantic
retrieval for selecting few-shot examples. We design a rationale-augmented DG framework that jointly generates distractors and their rationales for a given question-answer. Extensive experiments on six
benchmarks, with varying average distractor lengths and domains, demonstrate that prompting LLMs with few-shot examples substantially improves the performance compared to recent DG models. It outperforms recent approaches and achieves state-of-the-art results in generating reasoned distractors that align with human-labeled
benchmarks.
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Computation and Language
MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring
Salam Albatarni, May Bashendy, Sohaila Eltanbouly et al. (+1 more)
Automated Essay Scoring (AES) faces significant challenges in cross-
prompt settings, where models must generalize to unseen writing
prompts. To address this limitation, we propose MAPLE, a meta-learning framework that leverages prototypical networks to learn transferable representations across different writing
prompts. Across three diverse
datasets (ELLIPSE and ASAP (English), and LAILA (Arabic)), MAPLE achieves state-of-the-art performance on ELLIPSE and LAILA, outperforming strong baselines by 8.5 and 3 points in QWK, respectively. On ASAP, where
prompts exhibit heterogeneous score ranges, MAPLE yields improvements on several traits, highlighting the strengths of our approach in unified scoring settings. Overall, our results demonstrate the potential of meta-learning for building robust cross-
prompt AES systems.
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All authors Salam Albatarni, May Bashendy, Sohaila Eltanbouly, Tamer Elsayed
Computation and Language · Cross-listed from cs.AI
CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search
Hansi Zeng, Liam Collins, Bhuvesh Kumar et al. (+2 more)
Agentic search – the task of training
agents that iteratively reason, issue queries, and synthesize retrieved information to answer complex questions – has achieved remarkable progress through reinforcement learning (RL). However, existing approaches such as Search-R1, treat the
retrieval system as a fixed tool, optimizing only the
reasoning agent while the
retrieval component remains unchanged. A preliminary experiment reveals that the gap between an oracle and a fixed
retrieval system reaches up to +26.8% relative F1 improvement across seven QA
benchmarks, suggesting that the
retrieval system is a key bottleneck in scaling agentic search performance. Motivated by this finding, we propose CoSearch, a framework that jointly trains a multi-step
reasoning agent and a generative document ranking model via Group Relative Policy Optimization (GRPO). To enable effective GRPO training for the ranker – whose inputs vary across
reasoning trajectories – we introduce a semantic grouping strategy that clusters sub-queries by
token-level similarity, forming valid optimization groups without additional rollouts. We further design a composite reward combining ranking quality signals with trajectory-level outcome feedback, providing the ranker with both immediate and long-term learning signals. Experiments on seven single-hop and multi-hop QA
benchmarks demonstrate consistent improvements over strong baselines, with ablation studies validating each design choice. Our results show that joint training of the
reasoning agent and
retrieval system is both feasible and strongly performant, pointing to a key ingredient for future search
agents.
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All authors Hansi Zeng, Liam Collins, Bhuvesh Kumar, Neil Shah, Hamed Zamani
Computation and Language
PoliLegalLM: A Technical Report on a Large Language Model for Political and Legal Affairs
Yuting Huang, Yinghao Hu, Qian Xiao et al. (+7 more)
Large
language models (LLMs) have achieved remarkable success in general-domain tasks, yet their direct application to the legal domain remains challenging due to hallucinated legal citations, incomplete knowledge coverage, and weak structured
reasoning. To address these issues, we propose PoliLegalLM, a domain-specific large
language model tailored for political and legal applications. Our approach adopts a unified training framework that integrates continued pretraining, progressive supervised fine-tuning, and preference-based reinforcement learning to jointly enhance legal knowledge grounding, task
alignment, and
reasoning capability. We construct a large-scale, high-quality legal corpus and design a structured post-training pipeline, enabling the model to effectively learn domain-specific knowledge and adapt to diverse legal tasks. We evaluate PoliLegalLM on three representative
benchmarks, including LawBench, LexEval, and a real-world
dataset, PoliLegal. Experimental results demonstrate that PoliLegalLM achieves strong and consistent performance, outperforming competitive models of similar scale and remaining highly competitive with significantly larger models, while achieving the best results on real-world legal scenarios. These results highlight the effectiveness of our training paradigm and the practical value of domain-specific LLMs for real-world legal applications.
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All authors Yuting Huang, Yinghao Hu, Qian Xiao, Wenlin Zhong, Yiquan Wu, Taishi Zhou, Moke Chen, Changlong Sun, Kun Kuang, Fei Wu
Computation and Language
OPSDL: On-Policy Self-Distillation for Long-Context Language Models
Xinsen Zhang, Zhenkai Ding, Tianjun Pan et al. (+4 more)
Extending the effective context length of large
language models (LLMs) remains a central challenge for real-world applications. While recent post-training methods have made progress in long-context scaling, they either rely on high-quality supervision data or sparse sequence-level rewards, leading to unstable and inefficient optimization. We propose OPSDL, an On-Policy Self-
Distillation method for enhancing the Long-context capabilities of LLMs. Unlike other recent self-
distillation methods that inject privileged information and rely on the model's in-context learning ability to act as a teacher, OPSDL leverages the model's own inherently strong short-context capability as a self-teacher to supervise its own generation in long-context scenarios. The model first generates responses conditioned on the full long-context, then the self-teacher provides per-
token supervision signals via point-wise reverse KL divergence under the relevant extracted short-context. This dense
token-level signal encourages faithful use of relevant evidence and mitigates
hallucinations induced by irrelevant context. We evaluate OPSDL on long-context
benchmarks across a range of models from 7B to 32B parameters. Results show consistent and substantial improvements across varying context lengths, outperforming standard post-training approaches such as SFT and DPO with higher sample efficiency. Notably, these gains are achieved without degrading general short-context performance. These findings highlight the effectiveness of OPSDL as a scalable and stable approach for long-context learning.
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All authors Xinsen Zhang, Zhenkai Ding, Tianjun Pan, Run Yang, Chun Kang, Xue Xiong, Jingnan Gu
Computation and Language
ONTO: A Token-Efficient Columnar Notation for LLM Input Optimization
Harshavardhanan Deekeswar
Serialization formats designed for document interchange impose structural overhead that becomes prohibitive when large
language models consume operational data at scale. A modest
dataset of 1,000 IoT sensor readings serialized as JSON requires approximately 80,000
tokens - the majority spent on repeated field names, nested braces, and structural punctuation rather than semantic content. We present ONTO (Object Notation for
Token Optimization), a columnar notation that declares field names once per entity and arranges values in pipe-delimited rows with indentation-based hierarchy. This schema-once, data-many design eliminates per-record key repetition while preserving human readability and nested structure support. Evaluation across three synthetic operational
datasets demonstrates 46-51%
token reduction versus JSON, with stable scaling from 100 to 1,000 records. Controlled inference
benchmarks on Qwen2.5-7B show corresponding 5-10% latency improvement. Comprehension validation confirms no material degradation in LLM task accuracy across lookup, counting, extraction, and aggregation operations when format context is provided. Ablation analysis reveals that key repetition accounts for the majority of JSON overhead, with indentation costs in nested structures explaining the 4-percentage-point gap between flat and hierarchical data. ONTO occupies a previously unfilled position in the serialization landscape: columnar efficiency with hierarchical structure, optimized for LLM context windows rather than document interchange. Code and specification are available at https://github.com/harsh-aranga/onto.
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Computation and Language
CoAct: Co-Active LLM Preference Learning with Human-AI Synergy
Ruiyao Xu, Mihir Parmar, Tiankai Yang et al. (+3 more)
Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks. However, high-quality human-annotated preference data remains expensive and scarce. Existing methods address this challenge through either self-rewarding, which scales by using purely AI-generated labels but risks unreliability, or active learning, which ensures quality through oracle annotation but cannot fully leverage unlabeled data. In this paper, we present CoAct, a novel framework that synergistically combines self-rewarding and active learning through strategic human-AI collaboration. CoAct leverages self-consistency to identify both reliable self-labeled data and samples that require oracle verification. Additionally, oracle feedback guides the model to generate new instructions within its solvable capability. Evaluated on three
reasoning benchmarks across two model families, CoAct achieves average improvements of +13.25% on GSM8K, +8.19% on MATH, and +13.16% on WebInstruct, consistently outperforming all baselines.
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All authors Ruiyao Xu, Mihir Parmar, Tiankai Yang, Zhengyu Hu, Yue Zhao, Kaize Ding
Computation and Language
Answer Only as Precisely as Justified: Calibrated Claim-Level Specificity Control for Agentic Systems
Tianyi Huang, Samuel Xu, Jason Tansong Dang et al. (+2 more)
Agentic systems often fail not by being entirely wrong, but by being too precise: a response may be generally useful while particular claims exceed what the evidence supports. We study this failure mode as overcommitment control and introduce compositional selective specificity (CSS), a post-generation layer that decomposes an answer into claims, proposes coarser backoffs, and emits each claim at the most specific calibrated level that appears admissible. The method is designed to express uncertainty as a local semantic backoff rather than as a whole-answer refusal. Across a full LongFact run and HotpotQA pilots, calibrated CSS improves the risk-utility trade-off of fixed drafts. On the full LongFact run, it raises overcommitment-aware utility from 0.846 to 0.913 relative to the no-CSS output while achieving 0.938 specificity retention. These results suggest that claim-level specificity control is a useful uncertainty interface for agentic systems and a target for future distribution-free validity layers.
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All authors Tianyi Huang, Samuel Xu, Jason Tansong Dang, Samuel Yan, Kimberley Yin
Computation and Language · Cross-listed from cs.AI
Waking Up Blind: Cold-Start Optimization of Supervision-Free Agentic Trajectories for Grounded Visual Perception
Ashutosh Bajpai, Tamal Majumder, Akshay Nambi et al. (+1 more)
Small
Vision-
Language Models (SVLMs) are efficient task controllers but often suffer from visual brittleness and poor tool orchestration. They typically require expensive supervised trajectory tuning to mitigate these deficits. In this work, we propose Self-supervised Perception Enabled by Cascaded Tool Rollout
Alignment (SPECTRA), a supervision-free framework that bootstraps agentic capabilities via Coldstart Reinforcement Learning for SVLMs. SPECTRA enforces Soft Structured Multi-turn Rollouts, a topological constraint that directs
agents to explicitly sequence tool derived evidence before synthesis, effectively grounding
reasoning in visual observations. We employ a multi-objective reward signal that simultaneously maximizes task correctness, rollout structure, and tool utility, enabling
agent to self-discover robust behaviors without human preference labels. We further introduce Tool Instrumental Utility (TIU), a novel metric to quantify tool efficacy in the absence of ground truth. Extensive evaluations across composite and out-of-distribution (MMMU-Pro)
benchmarks demonstrate that SPECTRA boosts agentic trajectories, improving task accuracy by up to 5% and tool efficiency by 9%, enabling more efficient
multimodal agents that learn effectively from environmental interaction alone.
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All authors Ashutosh Bajpai, Tamal Majumder, Akshay Nambi, Tanmoy Chakraborty
Computation and Language
MoVE: Translating Laughter and Tears via Mixture of Vocalization Experts in Speech-to-Speech Translation
Szu-Chi Chen, I-Ning Tsai, Yi-Cheng Lin et al. (+2 more)
Recent
Speech-to-
Speech Translation (S2ST) systems achieve strong semantic accuracy yet consistently strip away non-verbal vocalizations (NVs), such as laughter and crying that convey pragmatic intent, which severely limits real-world utility. We address this via three contributions. First, we propose a synthesis pipeline for building scalable expressive
datasets to overcome the data scarcity limitation. Second, we propose MoVE, a Mixture-of-LoRA-Experts architecture with expressive-specialized adapters and a soft-weighting router that blends experts for capturing hybrid expressive states. Third, we show pretrained AudioLLMs enable striking data efficiency: 30 minutes of curated data is enough for strong performance. On English-Chinese S2ST, while comparing with strong baselines, MoVE reproduces target NVs in 76% of cases and achieves the highest human-rated naturalness and emotional fidelity among all compared systems, where existing S2ST systems preserve at most 14% of NVs.
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All authors Szu-Chi Chen, I-Ning Tsai, Yi-Cheng Lin, Sung-Feng Huang, Hung-yi Lee
Computation and Language
Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning
Raman Saparkhan, Majd Hawasly, Md Rizwan Parvez et al. (+1 more)
Self-consistency (SC) is a popular technique for improving the
reasoning accuracy of large
language models by aggregating multiple sampled outputs, but it comes at a high computational cost due to extensive sampling. We introduce a hybrid ensembling approach that leverages the complementary strengths of two distinct modes of
reasoning: Chain-of-Thought (CoT) and Program-of-Thought (PoT). We describe a general framework for combining these two forms of
reasoning in self-consistency, as well as particular strategies for both full sampling and early-stopping. We show that CoT-PoT ensembling not only improves overall accuracy, but also drastically reduces the number of samples required for SC by a factor of 9.3x. In particular, the majority of tasks (78.6%) can be addressed with only two samples, which has not been possible with any prior SC methods.
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All authors Raman Saparkhan, Majd Hawasly, Md Rizwan Parvez, Mohammad Raza