Computation and Language · Cross-listed from cs.AI
DeXposure-Claw: An Agentic System for DeFi Risk Supervision
Aijie Shu, Bowei Chen, Wenbin Wu et al. (+2 more)
Decentralized finance exposes supervisors to fast-moving, networked credit risks. General-purpose LLM
agents fit this setting poorly: they over-read weak evidence and recommend high-stakes interventions, while existing evaluations offer no regulator-aligned way to measure the resulting false alarms. We introduce DeXposure-Claw, a forecast-grounded agentic supervision system that routes LLM decisions through structured evidence: (1) DeXposure-FM, a graph time-series foundation model, forecasts future exposure networks; (2) deterministic monitors and stress scenarios then turn those forecasts into typed alerts, attribution signals, and scenario evidence; and (3) data-health and confidence gates constrain escalation before DeXposure-Claw emits auditable supervisory tickets with rationales. We further develop DeXposure-Bench, a six-axis evaluation harness, whose decision axis scores tickets against a regulator-aligned absolute-loss ground truth and an explicit false-intervention rate. Experiments on five years of weekly real data fully support our system. Code is at https://github.com/EVIEHub/DeXposure-Claw.
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All authors Aijie Shu, Bowei Chen, Wenbin Wu, Cathy Yi-Hsuan Chen, Fengxiang He
Computation and Language · Cross-listed from cs.AI
Thomas Bertolani, Davide Bucciarelli, Leonardo Zini et al. (+2 more)
Large
Language Models (LLMs) have revolutionized
language modeling through autoregressive generation, enabling strong performance across a wide range of tasks. Recently,
Diffusion Language Models (DLMs) have emerged as an alternative paradigm that generates text through iterative denoising rather than next-
token prediction, allowing parallel refinement of entire sequences. While numerous
diffusion-based architectures have been proposed, differences in evaluation protocols,
datasets, inference budgets, and generation hyperparameters make it difficult to compare their capabilities and understand the trade-offs they offer. In this work, we present a systematic experimental analysis of modern DLMs. Specifically, we evaluate eight state-of-the-art DLMs across eight
benchmarks spanning
reasoning, coding, translation, knowledge, and structured problem solving, while explicitly considering both generation quality and computational efficiency. Beyond downstream evaluation, we analyze the impact of key inference-time factors, including denoising steps, context length, block size, and parallel unmasking strategies, and complement large-scale experiments with controlled comparisons of smaller models trained under identical conditions. Our analysis highlights the strengths and limitations of
diffusion-based
language modeling across different tasks, architectures, and inference budgets. We show that the behavior of DLMs is strongly influenced by generation-time design choices, leading to distinct trade-offs between performance and computational efficiency. Overall, our study provides practical insights into the capabilities and deployment characteristics of contemporary DLMs.
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All authors Thomas Bertolani, Davide Bucciarelli, Leonardo Zini, Marcella Cornia, Lorenzo Baraldi
Computation and Language
Characterizing Narrative Content in Web-scale LLM Pretraining Data
Teagan Johnson, Elliott Ash, Andrew Piper et al. (+1 more)
The narrative composition of web-scale LLM pretraining corpora remains largely unexplored, even though narrative is a fundamental mode of human communication. We present the first fine-grained study of narrative features in Dolma, a 3-trillion-
token open pretraining corpus. Drawing on narrative theory, we design a framework spanning three core narrative elements (agency, setting, and events) operationalized as 11 interpretable dimensions. After curating and hand-annotating a diverse set of 400 passages, we create an LLM-labeled
dataset of 25K passages, and finally, we finetune and validate NarraBERT, two RoBERTa-based models for fine-grained narrative prediction. We apply NarraBERT to 13M passages, resulting in a new
dataset, NarraDolma. We find that narrative structure is measurable at scale across extremely heterogeneous data and narrative qualities are unequally distributed across pretraining sources, topics, and formats in ways that current data curation practices neither measure nor account for. Our framework,
dataset, and analyses provide a foundation for understanding how narrative qualities are distributed in LLM pretraining data.
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All authors Teagan Johnson, Elliott Ash, Andrew Piper, Maria Antoniak
Computation and Language · Cross-listed from cs.LG
Thermodynamic Signatures of Reasoning: Free-Energy and Spectral-Form-Factor Diagnostics for Hallucination Detection in Large Language Models
Salim Khazem
Hallucination detection in large
language models (LLMs) is deployment-critical, and recent work shows that the spectrum of
attention-derived graph
Laplacians carries strong signal about
reasoning quality. Prior spectral diagnostics, however, summarize the
Laplacian spectrum by a handful of
eigenvalues or hand-picked scalars, leaving most of its structure unused. We propose Free-Energy Signatures (Fes), a spectral descriptor that treats each layer's
attention Laplacian as a
Hamiltonian and extracts its thermodynamic potentials partition function, free energy, spectral
entropy, heat capacity together with the random-
matrix-theory (RMT) spectral form factor. We prove three results: (i)~Lipschitz stability of Fes under
attention perturbation; (ii)~an expressiveness result showing that Fes enriches finite spectral summaries and approximates moment-derived spectral functionals under explicit regularity and grid-resolution assumptions; and (iii)~a finite-sample PAC bound on the AUROC of a training-free detector built from Fes. Empirically, across six open-weight LLMs and six
benchmarks, a lightweight probe on Fes descriptors achieves the strongest aggregate AUROC among
attention-spectral baselines, improving over LapEig by $+6.5$ AUROC points and over GoR-4 by $+2.4$ points on average, while requiring no update to the underlying LLM. In the fully unsupervised setting, an RMT-deviation score achieves mean AUROC $0.71$, providing a label-free but weaker detector. A complementary RMT analysis shows that correct generations exhibit more Wigner-Dyson like spectral statistics, whereas
hallucinations exhibit more Poisson-like statistics. The anonymized code and config are provided in the supplementary material.
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Computation and Language · Cross-listed from cs.CV
Native Active Perception as Reasoning for Omni-Modal Understanding
Zhenghao Xing, Ruiyang Xu, Yuxuan Wang et al. (+8 more)
Passive models for long
video understanding typically rely on a "watch-it-all" paradigm, processing frames uniformly regardless of query difficulty, causing computational cost to grow with
video duration. Although interactive frameworks have emerged, they often rely on global pre-scanning, and their context cost still scales with
video length. We propose OmniAgent, the first native omni-modal
agent that formulates
video understanding as a POMDP-based iterative Observation-Thought-Action cycle. OmniAgent executes on-demand actions to selectively distill audio-visual cues into a persistent textual
memory, effectively decoupling
reasoning complexity from raw
video duration. To operationalize this, we introduce (1) Agentic Supervised Fine-Tuning to bootstrap native active perception via best-of-N trajectory synthesis with dual-stage quality control, and (2) Agentic Reinforcement Learning with TAURA (Turn-aware Adaptive Uncertainty Rescaled Advantage), which leverages turn-level
entropy to steer credit assignment toward pivotal discovery turns. Crucially, OmniAgent exhibits positive test-time scaling, where performance improves as the number of
reasoning turns increases, validating the efficacy of active perception. Empirical results across ten
benchmarks (e.g., VideoMME, LVBench) demonstrate that OmniAgent achieves state-of-the-art performance among open-source models. Notably, on LVBench, our 7B
agent outperforms the 10$\times$ larger Qwen2.5-VL-72B (50.5% vs. 47.3%).
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All authors Zhenghao Xing, Ruiyang Xu, Yuxuan Wang, Jinzheng He, Ziyang Ma, Qize Yang, Yunfei Chu, Jin Xu, Junyang Lin, Chi-Wing Fu, Pheng-Ann Heng
Computation and Language
Learning User Simulators with Turing Rewards
Yingshan Susan Wang, Cedegao E. Zhang, Linlu Qiu et al. (+5 more)
Learning to simulate human users in interactive settings could advance the training of
agent assistants, evaluation of personalization systems, research in the social sciences, and more. Existing approaches generally do so by training a large
language model (LLM) to match a single ground truth response, either by maximizing the log probability or by using a similarity reward. We instead propose Turing-RL: a Turing-Test-based reinforcement learning approach for training user simulator models. Turing-RL uses a discriminative Turing reward with an LLM judge to score how indistinguishable a generated response is from the real user's given the user's history, and the user simulator LLM learns to produce responses indistinguishable from what the user could have said with such rewards. Across two different domains–conversational chat and Reddit forum discussion–we find that Turing-RL consistently outperforms baseline methods on both LLM and human evaluation metrics. Our study suggests that optimizing for indistinguishability, rather than response matching, is effective for learning user simulators.
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All authors Yingshan Susan Wang, Cedegao E. Zhang, Linlu Qiu, Zexue He, Pengyuan Li, Alex Pentland, Roger P. Levy, Yoon Kim
Computation and Language
Freeing the Law with LOCUS: A Local Ordinance Corpus for the United States
Denis Peskoff, Joe Barrow, Christopher Vu et al. (+1 more)
Progress in legal AI increasingly depends on access to authoritative legal text at scale. Yet one of the most consequential layers of American law remains largely absent from existing machine-readable corpora: local ordinances. Local codes govern zoning, housing, business licensing, public health, noise, animal control, and many other domains of everyday regulation, but they are fragmented across vendor platforms designed for human browsing rather than bulk research access. We introduce LOCUS - the Local Ordinance Corpus for the United States - a comprehensive corpus and county-harmonized access layer for U.S. municipal and county ordinance codes. The raw corpus, available for release to researchers, represents nearly all publicly available municipal and county ordinance codes. The resulting raw corpus contains codes from 9,239 cities and counties. A smaller county-harmonized LOCUS access layer provides coverage for the largest 2,309 of 3,144 U.S. counties, accounting for a majority of the population. We use OCR to handle the myriad of document formats that have kept the law from being a public resource. We release the corpus with coverage metadata to support reproducibility, downstream legal AI research, and the incremental expansion of machine-readable access to local law. We train a collection of ModernBERT-based classifiers and scorers to facilitate analyzing U.S. local law among several dimensions, such as opacity and paternalism, that have not previously been studied at this scale. LOCUS-v1 and its derivative models are available at: https://huggingface.co/
datasets/LocalLaws/LOCUS-v1
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All authors Denis Peskoff, Joe Barrow, Christopher Vu, Diag Davenport
Computation and Language · Cross-listed from cs.AI
Rethinking Reward Supervision: Rubric-Conditioned Self-Distillation
Siyi Gu, Jialin Chen, Sophia Zhou et al. (+2 more)
Post-training of
reasoning language models is commonly driven by supervised
distillation and reinforcement learning with verifiable rewards.
Distillation often relies on chain-of-thought annotations that are expensive to obtain and may themselves be noisy, incomplete, or partially incorrect; even when the final solution is correct, an imperfect rationale can interfere with learning. Reinforcement learning with verified rewards, on the other hand, typically compresses evaluative feedback into a scalar signal, obscuring which aspects of a response should be improved. We propose Rubric-Conditioned Self-
Distillation, a framework that incorporates rubrics as structured, fine-grained feedback for on-policy self-
distillation. Our method conditions the teacher model on criterion-level rubrics and uses it to provide
token-level guidance on the student's own sampled trajectories. This design avoids treating a single reference rationale as the sole supervision target. Instead, rubrics specify what a strong response should satisfy, enabling more fine-grained credit assignment over the
reasoning process than scalar reward optimization. We instantiate this framework with a two-stage pipeline that first learns to generate task-specific rubrics and then trains a rubric-guided reasoner. We evaluate on a diverse suite of science
reasoning benchmarks and results show that rubric-conditioned self-
distillation effectively converts rubric-level criteria into
token-level guidance over the
reasoning process, surpassing GRPO by 1.0 points and OPSD by 0.9 points on average.
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All authors Siyi Gu, Jialin Chen, Sophia Zhou, Arman Cohan, Rex Ying
Computation and Language
Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Play
Leyang Shen, Yang Zhang, Xiaoyan Zhao et al. (+2 more)
Large
language model (LLM)-based multi-
agent systems (MAS) have demonstrated great potential in solving tasks with execution complexity, by distributing subtasks across cooperative
agents. However, this divide-and-conquer paradigm falls short on decision-making tasks that are also prevalent in the real world. These tasks require simultaneous
reasoning from the stances of all involved stakeholders whose decisions are mutually dependent and thus cannot be solved in isolation. We characterize this challenge as stance
entanglement, a form of decision complexity distinct from execution complexity. To address it, we propose Multi-
Agent Fictitious Play (MAFP), a novel MAS paradigm that represents stakeholder stances as
agents and formulates decision-making as an
equilibrium-seeking process. Built on the game-theoretic principle of fictitious play, MAFP iteratively updates each
agent's decision by best responding to the empirical mixture of other
agents' past decisions. This enables
agents to expose and address one another's weaknesses, progressively improving decision quality and robustness. We evaluate MAFP on challenging decision-making tasks that test the capability of deciding strategies for competitive scenarios prior to acting. MAFP outperforms both single-round and multi-round baselines on two complementary metrics, tournament strength and robustness, demonstrating its effectiveness in addressing stance
entanglement.
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All authors Leyang Shen, Yang Zhang, Xiaoyan Zhao, Chun Kai Ling, Tat-Seng Chua
Computation and Language
Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA
Ikram Belmadani, Oumaima El Khettari, Carlos Ramisch et al. (+3 more)
The development of large
language models (LLMs) has led to an increased focus on their adaptation to specialized domains and
languages, yet the effectiveness of domain adaptation strategies remains unclear. We present a study of medical domain adaptation using French medical question-answering (QA) as a case study. We compare continual pretraining (CPT), supervised fine-tuning (SFT), and their combination across three model families, multiple sizes, and three initialization types, explicitly disentangling adaptation effects from base model choice. We evaluate both multiple-choice (MCQA) and open-ended QA (OEQA) under greedy and constrained decoding using automatic metrics and LLM-as-a-Judge evaluation. For MCQA, CPT+SFT most often achieves the best scores, but gains over SFT are small and frequently not statistically significant, making SFT a strong and cost-effective default. For OEQA, CPT consistently improves overlap-based metrics, while SFT often degrades generation quality; instruction tuning and CPT+SFT are preferred by LLM-based evaluation. Cross-lingual experiments further show effective transfer from French adaptation to English
benchmarks. Overall, we provide practical guidelines for selecting adaptation strategies under computational constraints.
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All authors Ikram Belmadani, Oumaima El Khettari, Carlos Ramisch, Frederic Bechet, Richard Dufour, Benoit Favre
Computation and Language · Cross-listed from cs.LG
Structured Inference with Large Language Gibbs
Sanghyeok Choi, Henry Gouk and Esmeralda S. Whitammer
The knowledge encoded in large
language models (LLMs) can serve as a substrate for structured
reasoning over variables describing a complex world, but accessing this knowledge in a probabilistically coherent manner poses a difficult inference problem. We propose Large
Language Gibbs, a scheme for structured probabilistic inference that uses conditional distributions of an LLM as transition operators. Rather than sampling structured objects through single-pass autoregressive generation, we iteratively resample individual variables conditioned on others using an LLM's next-
token conditionals. This approach avoids order-dependent biases and produces a stationary distribution that reflects a compromise between all local conditionals. We apply this approach to sampling from synthetic distributions, consistent
reasoning tasks, and
Bayesian structure learning. The results suggest that the use of LLM conditionals in MCMC is a practical alternative to one-pass generation for structured probabilistic inference under a world prior accessible through noisy LLM conditionals.
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Computation and Language
DreamReasoner-8B: Block-Size Curriculum Learning for Diffusion Reasoning Models
Zirui Wu, Lin Zheng, Jiacheng Ye et al. (+5 more)
Block
diffusion language models accelerate decoding through parallel block-wise denoising, yet whether they can be reliably scaled for long chain-of-thought (CoT)
reasoning remains unresolved. To this end, we develop DreamReasoner-8B, an open-source block
diffusion reasoning model, and conduct a systematic study of how training and inference block sizes affect long-CoT
reasoning. Our analysis reveals a stark performance disparity: training with large block sizes yields remarkably poor
reasoning, whereas small block sizes preserve effective
reasoning. To bridge this granularity gap, we propose block-size curriculum learning, which gradually transitions training from fine-grained to coarse-grained block sizes, thereby overcoming this limitation and enabling strong
reasoning performance that generalizes across diverse inference block sizes. On mathematical and code
reasoning benchmarks, DreamReasoner-8B achieves results competitive with leading open autoregressive models such as Qwen3-8B. This work establishes a practical foundation for efficient,
reasoning-capable
diffusion language models. We release our model at https://github.com/DreamLM/DreamReasoner.
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All authors Zirui Wu, Lin Zheng, Jiacheng Ye, Shansan Gong, Xueliang Zhao, Yansong Feng, Wei Bi, Lingpeng Kong
Computation and Language · Cross-listed from cs.LG
STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability
Haipeng Luo, Qingfeng Sun, Songli Wu et al. (+4 more)
Reinforcement Learning with Verifiable Rewards
algorithms like GRPO have emerged as the dominant post-training paradigm for complex
reasoning in LLMs, yet commonly suffer from policy
entropy collapse during training. We conduct a first-order gradient analysis of
token-level
entropy dynamics under GRPO and identify a
token-level credit assignment mismatch: the per-
token entropy variation decomposes into the product of the trajectory-level advantage and an
entropy sensitivity function over the next-
token distribution, yielding an advantage-surprisal four-quadrant structure and a near-criticality property. Motivated by it, we propose STARE (Surprisal-guided
Token-level Advantage Reweighting for policy
Entropy stability), which identifies
entropy-critical
token subsets via batch-internal surprisal
quantiles, selectively reweights their effective advantages, and incorporates a target-
entropy closed-loop gate for stable
entropy regulation. Across model scales from 1.5B to 32B and three task families (Short CoT, Long CoT, and Multi-Turn Tool Use), STARE sustains stable RL training over thousands of steps while maintaining policy
entropy within the target band. On AIME24 and AIME25, STARE outperforms DAPO and other competitive baselines by 4%-8% in average accuracy, with reflection
tokens and response length growing in tandem, indicating sustained exploration-exploitation balance that further unlocks RL training potential.Code is available at https://github.com/hp-luo/STARE.
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All authors Haipeng Luo, Qingfeng Sun, Songli Wu, Can Xu, Wenfeng Deng, Han Hu, Yansong Tang
Computation and Language
RECOM: A Validity Discrimination Tradeoff in Automatic Metrics for Open Ended Reddit Question Answering
Pushwitha Krishnappa, Amit Das, Vinija Jain et al. (+2 more)
Automatic metrics are the default for evaluating LLM-generated text, yet a metric is quietly asked to do two jobs: tell genuine content
alignment from surface coincidence (validity), and tell a better system from a worse one (discriminative power). On open-ended, opinion-driven question answering, the two are in tension. We introduce RECOM (Reddit Evaluation for Correspondence of Models), a contamination-free evaluation
dataset of 15,000 r/AskReddit questions (September 2025), each paired with its authentic community replies, which postdate every evaluated model's training cutoff. Scoring five open-source LLMs (7–10B) against every reply each metric paired with a random-derangement noise floor we find that no metric does both jobs well. Cosine similarity separates real from random answers (Cohen's $d \approx 2$) but cannot rank the five models ($|d| < 0.1$); BERTScore precision appears to rank the models (raw $|d|$ up to 0.63), but once response length is controlled this collapses to $|d| = 0.09$ and its validity is weak ($d \approx 0.8$, versus cosine's $\approx 2$). Because every metric scores the same outputs, this validity–discrimination tradeoff is a property of the metrics, not the models, and we argue it stems from representation design. Three independent LLM judges reproduce the validity gap and likewise separate the five models only weakly. We recommend reporting metrics on both axes, with an explicit random-baseline floor. RECOM is publicly available at https://anonymous.4open.science/r/recom-D4B0
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All authors Pushwitha Krishnappa, Amit Das, Vinija Jain, Aman Chadha, Tathagata Mukherjee
Computation and Language
Language Models as Interfaces, Not Oracles: A Hybrid LLM-ML System for Pediatric Appendicitis
Soheyl Bateni and Maryam Abdolali
Large
language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to
prompts, information order, and plausible but incorrect outputs. Structured machine-learning models offer more stable risk prediction, yet they require tabular inputs that are difficult to integrate with narrative clinical workflows. We present ClaMPAPP (Clinical
Language-assisted Machine-learning Pipeline for Appendicitis), a hybrid system that uses an LLM as an interface rather than as the final decision-maker. ClaMPAPP extracts schema-constrained clinical features from note-like narratives, applies deterministic plausibility checks, and passes validated features to an XGBoost classifier trained on clinical, laboratory, and ultrasound variables. We evaluated ClaMPAPP on two independent pediatric appendicitis cohorts from German hospitals and compared it with end-to-end LLM baselines, including open-source and proprietary models. To preserve ground truth while testing free-text input, narratives were generated from structured electronic health records through template rendering and constrained LLM rewriting, with additional sentence-order permutation to assess positional robustness. ClaMPAPP achieved the strongest overall diagnostic performance in both internal and external validation while minimizing missed appendicitis cases, the key safety concern in acute triage. End-to-end LLMs showed unstable sensitivity-specificity trade-offs and greater degradation under narrative reordering. These results support an LLM-as-interface, ML-as-predictor design that separates natural-
language usability from predictive inference and provides a more auditable pathway for clinical decision support.
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Computation and Language
Dango: A Strictly L1-Only Large Language Model for Studying Second Language Acquisition
Shiho Matta, Yin Jou Huang, Fei Cheng et al. (+3 more)
We introduce Dango, a 1.8B-parameter large
language model designed for controlled studies of L1-to-L2 (Japanese-to-English) transfer in second
language acquisition (SLA). While previous studies have explored SLA in
language models, they have predominantly relied on smaller or non-decoder models, limiting their ability to generate open-ended text and reducing their suitability as practical L2 simulators. We identify a key challenge when scaling models to this size: L2 contamination within the "monolingual" pretraining corpus used for L1 acquisition. To address this, we propose a filtering method to reduce premature exposure to English while preserving realistic, minimal exposure. We then fine-tune the model on LLM-generated L2-learning lessons to simulate the L2 acquisition process. Our evaluations confirm that Dango develops human-like L2 production patterns, outperforming both unfiltered and standard
multilingual baselines. We release the model, data, and code to facilitate reproducible computational SLA research and learner-facing applications.
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All authors Shiho Matta, Yin Jou Huang, Fei Cheng, Takashi Kodama, Hirokazu Kiyomaru, Yugo Murawaki
Computation and Language · Cross-listed from eess.AS
IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages
Sakshi Joshi, Dhruv Subhash Rathi, Sanskar Singh et al. (+4 more)
AudioLLMs enable
speech recognition conditioned on textual
prompts such as domain descriptions or entity lists. However, it remains unclear whether these models genuinely utilise such context or rely on parametric knowledge learned during pretraining. Existing
benchmarks cannot answer this question because they evaluate transcription under fixed prompting conditions and rarely include explicit contextual inputs. We introduce IndicContextEval, a 56-hour
multilingual benchmark of natural
speech from 555 speakers across 8 Indian
languages and 23 professional domains. We design a 7-level prompting framework that progressively introduces contextual signals, including metadata, natural-
language descriptions, entity lists in English and native script, and
adversarial prompts with incorrect entities. Evaluating five models reveals substantial differences in context utilisation behaviour, highlighting the need for explicit evaluation of contextual grounding in AudioLLMs.
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All authors Sakshi Joshi, Dhruv Subhash Rathi, Sanskar Singh, Eldho Ittan George, R J Hari, Kaushal Bhogale, Mitesh M. Khapra
Computation and Language · Cross-listed from cs.AI
Human-AI Coevolution Dynamics: A Formal Theory of Social Intelligence Emergence Through Long-Term Interaction
Jingyi Zhou, Senlin Luo and Haofan Chen
Current conversational AI systems have made significant progress in
language generation, personalization, and long-context interaction. However, most existing methods model social behavior through isolated components such as
emotion modeling,
memory retrieval, or persona conditioning, lacking a unified framework to explain the emergence of stable social relationships and social intelligence in long-term human-AI interaction.To address this, we propose the Human-AI Coevolution Dynamics Framework (HACD-H), a formal model of human-AI interaction as a self-organizing social cognitive system. HACD-H integrates emotional adaptation, relational organization, social
memory, and personality consistency into a unified dynamical framework and introduces principles including multi-timescale social cognition, relational attractors, trust basins, developmental phase transitions, and social cognitive energy dynamics.We construct a conversational
dataset with approximately 14,700 interaction turns and develop a theory-driven empirical evaluation framework. Results reveal a hierarchy of temporal persistence in social cognition, stable relational attractors, phase-transition-like developmental patterns, and a structured social cognitive energy landscape. Social intelligence shows a significant negative correlation with social cognitive energy (r = -0.391, p < 0.001), and interaction trajectories exhibit progressive energy reduction over time.These findings suggest that social intelligence emerges from long-term social cognitive coevolution rather than isolated conversational capabilities. HACD-H provides a unified theoretical foundation for modeling adaptive human-AI social interaction and developing socially intelligent AI systems.
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Computation and Language · Cross-listed from cs.CV
Urdu Katib Handwritten Dataset: A Historical Document Dataset for Offline Urdu Handwritten Text Recognition with CRNN-Based Baseline Evaluation
Ramza Basharat and Muhammad Usman Ali
Automatic Handwritten Text Recognition (HTR) is inherently a challenging task, and its complexity is further increased when dealing with cursive scripts. Although significant efforts have been made on various cursive scripts, research regarding Urdu Handwritten Text Recognition (UHTR) has been relatively limited. This lag of research is primarily due to the unique challenges posed by its script, and the scarcity and unavailability of
benchmark datasets. Therefore, to advance research in UHTR, this study presents a specialized real
dataset called the Urdu Katib Handwritten
Dataset (UKHD). To the best of our knowledge, this is the first offline Urdu handwritten text lines
dataset specifically curated from the materials written by Katibs in historical times. It encompasses a diverse range of flat nib writing variations in the Nastalique calligraphic style. Additionally, the effectiveness of different CRNN-based hybrid models has been evaluated to identify the optimal architecture for Urdu Katib Handwriting Recognition (UKHR). Among the analyzed models, the CNN-BGRU-CTC model showed more robust performance, with low Character Error Rate (CER) and Word Error Rate (WER). This research work aims to support and encourage the research community in developing a robust recognition system for preserving Urdu handwritten literature.
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Computation and Language · Cross-listed from cs.SE
Written by AI, Managed by AI: Semantic Space Control and Index Sickness Elimination Across 391 Consecutive Sessions
Hui Zhang and Shuren Song
The prevailing engineering intuition for addressing conceptual drift in long-horizon LLM collaboration is to trade more formal constraints for more reliable outputs – designing symbolic identifier systems, accumulating defensive rules in System
Prompts, expanding context windows. Our engineering record shows that in long-horizon settings, this direction may produce effects contrary to design intent. Using action research methods in a real software project (Bang-v3) spanning approximately one month and 391 collaborative sessions, we document and analyze the failure process of these strategies. When the symbolic system exceeds a complexity threshold, LLMs do not become more accurate – instead, they abandon genuine understanding of business semantics, retreat to self-referential
reasoning within the symbolic layer, and generate outputs that appear internally consistent but are physically disconnected from reality. We name this failure pattern "Index Sickness," and its canonical manifestation "Phantom Legislation." We name the underlying principle the "Pang Principle (Semantic Vitality Law)": natural
language carrying explicit purpose conveys far greater information quality than symbolic expression. From this, we design and validate its physical engineering mechanism: "Baseline-Log Physical Separation." In the same project, this mechanism reduced AI Instructions volume by ~75%, and across the subsequent ~150 sessions, no recurrence of Index Sickness was observed. A bilingual companion version (Chinese) is included as supplementary material.
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Computation and Language
Leadership as Coordination Control: Behavioral Signatures and the Recovery-Advantage Boundary in Multi-Agent LLM Teams
Haewoon Kwak
Team science holds that leadership is contingent: it helps only under specific conditions. We ask the same question of multi-
agent LLM teams: under what measurable conditions does process-level coordination control add value, and do those conditions match what team science predicts? This takes measurement, not just accuracy: we use behavioral signatures (majority lock-in, exploration, recovery from an incorrect round-0 consensus) and per-action ablations, clean because each controller is an explicit action set. We operationalize three classical leadership styles (transactional, transformational, situational) as controllers over a shared action vocabulary (explore, revise, accept, synthesize). A matched controller with the same actions but an arbitrary rule recovers no better than majority voting. Across four task regimes and three open-weight model families, no controller dominates by accuracy. Transactional control matches a shared round-0 vote on all 12 combinations to within 1.3pp, and accuracy gains appear only on the single llama-4-scout social combination, where the round-0 majority is unreliable. Against the stronger flat baseline, only situational still gains (+8pp). A recovery-advantage account, tested with four boundary probes, says a controller beats plain interaction only where the round-0 majority is unreliable, the task is recoverable, and undirected interaction does not already repair it. Because these conditions map onto contingency theory (leadership substitutes, path-goal redundancy, the readiness gap), a largely null accuracy result is what the theory predicts, not a failure of the controllers. We advance this readiness-gap boundary as an explanatory hypothesis rather than an established general principle. Its null side is corroborated across every other combination, including under held-out prediction, while its positive side is observed in a single combination.
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Computation and Language
Which Sections of a Research Paper Best Reveal Its Research Methods? Evidence from Library and Information Science
Qiuyu Fang, Jiayi Hao and Chengzhi Zhang
Research methods are essential carriers of knowledge contribution in academic papers. Automatic multi-label classification of research methods can support knowledge services such as method
retrieval, review generation, and research intelligence analysis. While existing studies primarily rely on titles and abstracts, abstracts often provide only limited methodological information, whereas utilizing full-text content faces challenges related to excessive length and information redundancy. Therefore, this paper proposes a segment combination strategy by partitioning the full-text content according to its physical postion. Using an annotated corpus of 1,954 full-text articles from three representative journals in Library and Information Science (JASIST, LISR, and JDoc), we evaluate the classification performance of various segments and their combinations across multiple models. Experimental results indicate that methodological information is distributed unevenly within the full-text content, with the middle-to-late and final segments exhibiting greater discriminative power. Furthermore, integrating bibliographic metadata with cross-segment combination strategies effectively enhances classification performance.
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Computation and Language
Sumi: Open Uniform Diffusion Language Model from Scratch
Mengyu Ye, Keito Kudo, Wataru Ikeda et al. (+3 more)
Diffusion models have become a promising alternative to autoregressive models. Among these, uniform
diffusion language models (UDLMs) permit any
token to be updated at any step, in principle enabling more flexible generation. However, no UDLM has yet been pretrained from scratch at both large parameter scale and large
token budget. Both autoregressive modeling and masked
diffusion modeling already have capable models at scale that the community can study and build on; uniform
diffusion has none. A scratch-pretrained UDLM at scale would provide a clean reference point for studying scaling behavior, generation dynamics, controllability, and trade-offs against established autoregressive and masked
diffusion models. To this end, we introduce Sumi ("ink" in Japanese), a fully open 7B uniform
diffusion language model pretrained from scratch on 1.5T
tokens. Sumi performs competitively with autoregressive models trained at comparable
token budgets on knowledge,
reasoning, and coding
benchmarks, while under-performing on commonsense
benchmarks, where our education-heavy data mixture is a likely contributor. We release our model weights, checkpoints, and full training recipe, including a complete specification of the data mixture over publicly available corpora. We hope this release enables the community to study native uniform
diffusion at scale and catalyzes work on its as-yet poorly understood aspects.
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All authors Mengyu Ye, Keito Kudo, Wataru Ikeda, Ryosuke Matsuda, Keisuke Sakaguchi, Jun Suzuki
Computation and Language
Zhuoran Li, Rui Xu, Jian Yang et al. (+8 more)
Model merging is an effective technique for composing the capabilities of a
multilingual model and a
reasoning model. It has achieved promising generalization in
multilingual reasoning tasks by aligning feature spaces of different models. However, the merged single model often fails to address the conflicts between source models, leading to suboptimal performance. In other words, the one-size-fits-all merging strategy may not align with the characteristics of different inputs which may require prioritizing certain models over others. To this end, we propose a Steerable Model Merging (ST-Merge) framework to modulate the contribution of each source model. To realize this idea, we introduce a gated cross-
attention mechanism to weight or filter the two attended source models in an adaptive manner. Extensive experiments demonstrate that ST-Merge consistently outperforms multiple strong baselines on four
multilingual reasoning benchmarks across 21 different
languages.
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All authors Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming Li, Xiaojie Wang
Computation and Language
G-IdiomAlign: A Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment
Fengying Ye, Yanming Sun, Runzhe Zhan et al. (+3 more)
Idioms are difficult to transfer across
languages due to their non-compositionality and weak surface-form grounding, making literal mappings unreliable. We present G-IdiomAlign, a gloss-pivoted
benchmark where each idiom is anchored by an English gloss from Wiktionary. We further construct a high-confidence reference
alignment set for reproducible evaluation. G-IdiomAlign supports two protocols: (1) a controlled Multiple-Choice Idiom Equivalence with typed distractors for error attribution; and (2) a Gloss-Contrastive Generation contrasting No-gloss and With-gloss inputs to isolate the effect of an explicit semantic pivot. Across diverse LLMs, a bias to literal translation is a dominant failure mode, especially when the target is a low-resource
language. Glosses consistently improve Gloss-Contrastive Generation under an
embedding-based semantic proxy, but performance remains modest, indicating substantial headroom in the open output space. Subsequent analysis on Qwen3-8B further suggests that cross-condition differences are concentrated more in
attention heads than in layers, while better With-gloss generations coincide with stronger gloss anchoring.
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All authors Fengying Ye, Yanming Sun, Runzhe Zhan, Zheqi Zhang, Lidia S. Chao, Derek F. Wong
Computation and Language
Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering
Yafeng Wu, Huu Hiep Nguyen, Thin Nguyen et al. (+1 more)
Recent advances in large
language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-
language question answering. However, directly feeding raw numerical series into LLMs suffers from a tokenization bottleneck: Byte Pair Encoding fragments continuous values into unstable
tokens whose
embeddings lack meaningful metric structure, resulting in the loss of magnitude, scale, and trend information. Prior methods use patch-based encoders that split the series into fixed windows, locking in one granularity that breaks patterns and hides exact timesteps, through a separate module that rarely transfers across
datasets with different lengths or sampling rates. To address this challenge, we propose CADE (Contrastive
Alignment with Direct
Embedding), a novel framework for TSQA built upon two key components: direct timestep
embedding and semantic
alignment. The proposed framework maps each timestep directly into the LLM
embedding space through a point-wise linear encoder and MLP projector, preserving exact index-level access while eliminating the need for patching and padding. To further bridge the semantic gap between time-series and
language representations, we introduce a novel one-directional supervised contrastive loss that aligns time-series
embeddings with frozen class-name text anchors. Experimental results on the public Time-MQA
benchmark demonstrate that our framework consistently improves performance across six TSQA tasks, outperforming both open-source and proprietary LLM baselines.
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All authors Yafeng Wu, Huu Hiep Nguyen, Thin Nguyen, Hung Le
Computation and Language · Cross-listed from eess.AS
Mitigating Scoring Errors and Compensating for Nonverbal Subtests in Speech-Based Dementia Assessment
Franziska Braun, Christopher Witzl, Andreas Erzigkeit et al. (+4 more)
Early detection of cognitive impairment relies on neuropsychological tests to minimize subjectivity by assessing multiple cognitive domains.
Speech-based evaluation can support diagnostics and improve accessibility, but transcription errors and the omission of nonverbal subtests (e.g., motor skills) limit accuracy. Beyond conventional test scores,
speech-derived features can provide additional insights into cognitive status. This study investigates the
speech-based evaluation of the German "Syndrom-Kurz-Test," a standardized dementia screening test comprising verbal and motor subtests. We train models that integrate transcript-derived scores and Whisper
embeddings per verbal subtest to reduce scoring errors. To compensate for missing motor subtests, we then leverage these fused representations to approximate expert overall ratings. Despite omitting subtests, our models strongly correlate with expert ratings and efficiently and accurately discriminate between cognitive status groups.
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All authors Franziska Braun, Christopher Witzl, Andreas Erzigkeit, Hartmut Lehfeld, Thomas Hillemacher, Tobias Bocklet, Korbinian Riedhammer
Computation and Language
GraphPO: Graph-based Policy Optimization for Reasoning Models
Yuliang Zhan, Xinyu Tang, Jian Li et al. (+7 more)
Reinforcement Learning with Verifiable Rewards (RLVR) has become a standard paradigm for enhancing the capability of large
reasoning models. RLVR typically samples responses independently and optimizes the policy using from final answers. This paradigm has two limitations. First, independently responses often contain similar intermediate
reasoning steps, causing redundant exploration and wasted computation. Second, sparse final-answer rewards make it hard to identify useful steps. Tree-based methods partly address this problem by sharing prefixes and comparing branches from the same prefix to provide fine-grained signals. However, tree branches are still expanded independently. When different branches reach similar
reasoning states, they cannot share information and repeat similar exploration. Moreover, tree-based methods ignore such dispersion and only perform local comparisons within separate branches, which can lead to higher variance in advantage estimation. To address this challenge, we propose GraphPO (Graph-based Policy Optimization), a novel RL framework that represents rollouts as a directed acyclic graph, with
reasoning steps as edges and semantic states summarized from the
reasoning paths as nodes. GraphPO merges semantically equivalent
reasoning paths into equivalence classes, allowing them to share suffixes and reallocating budget away from redundant expansions to diverse exploration. Furthermore, we assign efficiency advantages to incoming edges and correctness advantages to outgoing edges, thereby improving inference efficiency while deriving process supervision from outcome. Theory shows that GraphPO reduces advantage-estimation variance and enhances
reasoning efficiency. Experiments on three LLMs across
reasoning and agentic search
benchmarks show that GraphPO consistently outperforms chain- and tree-based baselines with the same
token budgets or response budgets.
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All authors Yuliang Zhan, Xinyu Tang, Jian Li, Dandan Zheng, Weilong Chai, Jingdong Chen, Jun Zhou, Ge Wu, Wenyue Tang, Hao Sun
Computation and Language · Cross-listed from cs.AI
Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agents
Emmanuel Aboah Boateng, Kyle MacDonald, Amardeep Kumar et al. (+2 more)
Production LLM
agents increasingly depend on real-time search, yet native search grounding bundles
retrieval policy, provider choice, evidence injection, cost, latency, and generation behavior behind a single model-provider boundary. This coupling makes grounding hard to inspect, tune, reuse, or port, and can trigger Search-Induced Verbosity that breaks strict output contracts. We present Decoupled Search Grounding (DSG), a vendor-agnostic boundary that moves grounding outside the
reasoning model through an MCP-compatible gateway, exposing provider routing, source-aware context rendering, configured fallback,
retrieval-depth control, and exact plus semantic caching as first-class controls. Across five frontier models on SimpleQA, FreshQA, and HotpotQA, native search leads on recency-sensitive FreshQA, but DSG exposes a stronger frontier when control matters: on SimpleQA it nearly matches native accuracy (86.1% vs. 87.7%) at 91% lower search cost, preserves concise answer contracts, and reaches a 99.4% warm-cache hit rate with 68% lower latency. Deployed as a shared production grounding layer for large-scale agentic workloads with interchangeable models, DSG matches native-search accuracy on an e-commerce query-understanding (QIU) workload while cutting search cost by over 98%. Real-time grounding is best treated as an optimizable interface boundary, not a fixed model feature.
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All authors Emmanuel Aboah Boateng, Kyle MacDonald, Amardeep Kumar, Siddharth Kodwani, Sudeep Das
Computation and Language
SenFlow: Inter-Sentence Flow Modeling for AI-Generated Text Detection in Hybrid Documents
Jingkun Luo, Yifan Sun, Da-Tian Peng et al. (+1 more)
Sentence-level AI-generated text detection (S-AGTD) for hybrid documents, where humans and LLMs co-author one text, faces two gaps: existing methods classify each sentence in isolation, discarding inter-sentence dependencies, and existing
benchmarks omit the newest generation of generators. We construct MOSAIC, a
benchmark of 16,000 hybrid documents over PubMed and XSum, generated by DeepSeek-V3.2 and Kimi K2 under stringent quality controls including a perplexity-consistency filter absent from prior
benchmarks. We recast S-AGTD as structured prediction over the document sentence sequence and instantiate it as SenFlow, integrating graph-based inter-sentence propagation with linear-chain CRF decoding in a single document-level pass over a sentence graph. SenFlow reaches state-of-the-art performance on MOSAIC, with a +4.15 pp average Macro-F1 margin on cross-domain transfer, the hardest of three protocols of increasing difficulty. We further find that even after the perplexity filter equalizes overt cues, AI insertions retain a generator-dependent sentence-length gap that sentence-level detectors still exploit. Code and data: https://github.com/luojingkun22/SenFlow
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All authors Jingkun Luo, Yifan Sun, Da-Tian Peng, Guanxiong Pei