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Tag: cs.CL

  • Contextual Correction of Parallel Draftsfor Speculative Decoding

    Contextual Correction of Parallel Draftsfor Speculative Decoding

    [Submitted on 8 Oct 2026]

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    Abstract:Speculative decoding accelerates large language model inference by using a lightweight drafter to propose multiple tokens that are subsequently verified in parallel by the target model. Recent diffusion-based drafters make this paradigm particularly attractive: deep attention-based neural backbones provide strong predictions, while drafting multiple future positions in parallel avoids the sequential cost of autoregressive generation. This parallelism, however, comes at a cost. Tokens within a draft block are predicted without access to the realized identities of preceding draft tokens, removing intra-block dependencies that would naturally be available under autoregressive generation. We introduce DCOCO (DFlash Contextual Corrector), a lightweight mechanism that restores these dependencies while preserving parallel drafting. Rather than committing to each position’s top prediction, DCOCO retains multiple candidate tokens and evaluates their contextual compatibility across adjacent draft positions in parallel, enabling contextually informed token selection at low additional cost. Across four verifier backbones and ten benchmarks, DCOCO achieves the highest average throughput in every evaluated configuration, spanning greedy and stochastic decoding with and without extended reasoning, with speedups of up to 6.6x over vanilla decoding. Its advantage persists under concurrent serving: DCOCO outperforms the strongest competing method by 6-23% across 4-32 concurrent requests, while other sequential correctors fall below uncorrected DFlash at these loads. These results demonstrate that recovering intra-block dependencies through parallel contextual scoring delivers consistent acceleration across decoding regimes and serving loads.

    Submission history

    From: Roy Eisenstadt [view email]
    [v1] Thu, 8 Oct 2026 18:01:03 UTC (236 KB)

  • When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Tasks

    When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Tasks

    [Submitted on 9 Oct 2026]

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    Abstract:Web agents increasingly browse, interpret, and act on web content on behalf of users. In many realistic workflows, however, the visual evidence they consume is not neutral: charts and dashboards can be misleading, shaping judgments without issuing any explicit malicious instruction. Unlike chart question answering, Web Agents treat charts as intermediate evidence whose misinterpretation can propagate into concrete downstream actions — a reliability risk that remains underexplored. We introduce MisVisAgentBench, the first paired executable benchmark for evaluating how misleading visualizations affect Web Agent execution. It comprises 280 task instances in matched clean–misleading pairs that differ only in chart evidence. We further provide a diagnostic framework that isolates misleading-induced failures from baseline task difficulty (Zhu et al., 2025) and captures execution-level effects such as extra browsing steps and decision hesitation. Across thirteen vision-language Web Agents, replacing clean charts with misleading counterparts reduces the average task success rate by more than 34 percentage points. Moreover, these failures predominantly follow the targeted misleading action branch, suggesting that deceptive charts systematically redirect rather than randomly disrupt agent decisions. These results reveal that visualization robustness is a critical yet understudied dimension of Web Agent safety and reliability.

    Submission history

    From: Yisheng Li [view email]
    [v1] Fri, 9 Oct 2026 00:03:13 UTC (2,475 KB)

  • ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios

    ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios

    [Submitted on 13 Oct 2025 (v1), last revised 8 Oct 2026 (this version, v2)]

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    Abstract:Large language models (LLMs) are increasingly under scrutiny for perpetuating identity-based discrimination in high-stakes domains such as hiring, particularly against people with disabilities (PwD). However, existing research remains largely Western-centric, overlooking how intersecting forms of marginalization–such as gender and caste–shape experiences of PwD in the Global South. We conduct a comprehensive audit of six LLMs across 2,820 hiring scenarios spanning diverse disability, gender, nationality, and caste profiles. To capture subtle intersectional harms and biases, we introduce ABLEIST (Ableism, Inspiration, Superhumanization, and Tokenism), a set of five ableism-specific and three intersectional harm metrics grounded in disability studies literature. Our results reveal significant increases in ABLEIST harms towards disabled candidates–harms that many state-of-the-art models failed to detect. These harms were further amplified by sharp increases in intersectional harms (e.g., Tokenism) for gender and caste-marginalized disabled candidates, highlighting critical blind spots in current safety tools and the need for intersectional safety evaluations of frontier models in high-stakes domains like hiring.

    Submission history

    From: Hayoung Jung [view email]
    [v1] Mon, 13 Oct 2025 04:18:23 UTC (1,493 KB)
    [v2] Thu, 8 Oct 2026 22:37:37 UTC (1,389 KB)

  • How AI Writing Assistance Distorts Perceptions of AI Users and Their Opinions

    How AI Writing Assistance Distorts Perceptions of AI Users and Their Opinions

    [Submitted on 24 Apr 2026 (v1), last revised 8 Oct 2026 (this version, v3)]

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    Abstract:Hundreds of millions of people use AI for writing assistance. We asked how AI distorts perceptions of AI users and their opinions. Writers (N=2,939) wrote political opinion paragraphs with and without AI assistance. Separate readers (N=11,091) blindly evaluated these paragraphs and made inferences about the writers. With AI, writers seemed more opinionated, competent, positive, and demographically privileged. Writers objected to many of these distortions yet preferred AI-assisted text even when made aware of them. Reward models trained on our data mitigated objectionable distortions but reduced user acceptance. Additional readers (N=8,798) trusted AI-assisted writers more and were more persuaded by AI writing when AI was more distortive. Overall, distortions from AI writing assistance are pervasive, persistent under human oversight, and likely to have consequential effects on human behaviours and attitudes.

    Submission history

    From: Paul Röttger [view email]
    [v1] Fri, 24 Apr 2026 12:31:11 UTC (1,354 KB)
    [v2] Mon, 29 Jun 2026 11:51:27 UTC (1,436 KB)
    [v3] Thu, 8 Oct 2026 19:19:51 UTC (3,683 KB)

  • Recurrent Harness for Agentic Continual Learning

    Recurrent Harness for Agentic Continual Learning

    [Submitted on 8 Oct 2026]

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    Abstract:Language model agents increasingly operate over long interaction streams, requiring online adaptation to distribution shifts through continual learning. However, existing agent harnesses typically either evolve their states only through forward information flow or rely on additional replay or validation data for harness optimization. To address this, we propose ReHarness, a recurrent harness framework for agentic continual learning that complements forward read and write with backward revision under a strictly online streaming protocol. We ask what information could have been captured differently in the harness state to better inform the current instance in the stream. Specifically, after each instance, we leverage the resulting trajectory as feedback to revise the reflections retrieved for that instance, thereby propagating feedback backward through the textual harness state. Across three continual learning benchmarks and three language model backbones, ReHarness consistently outperforms strong raw-context baselines, memory- and reflection-based methods, and prompt optimization approaches. Further analysis shows that ReHarness produces source-grounded and meaningful revisions, corrects overgeneralized or outdated experience under distribution shifts, and improves both stability and plasticity during continual learning.

    Submission history

    From: Yuxin Xiao [view email]
    [v1] Thu, 8 Oct 2026 19:44:43 UTC (422 KB)

  • S-Space: Exploring Spatial Workspace in Multimodal Language Models

    S-Space: Exploring Spatial Workspace in Multimodal Language Models

    [Submitted on 9 Oct 2026]

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    Abstract:Multimodal language models (MLLMs) exhibit promising spatial reasoning capabilities, yet the internal mechanisms supporting them remain poorly understood. We uncover S-Space, a linear representation subspace in which object-token representations encode continuous coordinates along horizontal, vertical, and distance axes. Although we identify these axes by differentiating spatial-answer scores with respect to object-token activations, S-Space is intrinsic rather than induced by specific data or prompts, remaining readable under non-spatial questions. We show that S-Space functions as an internal spatial workspace of MLLMs. Perception encodes object locations into it, forming spatial representations that can be directly read out to support accurate spatial judgments. Internal reasoning transforms it for changing viewpoints. Verbal responses selectively access it, evidenced by causal interventions that alter spatial judgments while largely preserving non-spatial responses. Case studies further show that S-Space assigns locations to unseen entities and the observer, and connects physical directions with abstract linguistic meanings, suggesting both the capabilities and limitations of large-scale language training. Finally, we use S-Space as a diagnostic tool for spatial reasoning failures. Direction-word readouts reveal systematic coupling between cardinal and image-relative reference frames. More importantly, a perception-computation decomposition exposes a key bottleneck: MLLMs can form capable spatial representations, yet fall short in the spatial transformations required to reason reliably across viewpoint changes.

    Submission history

    From: Jialong Wu [view email]
    [v1] Fri, 9 Oct 2026 12:48:41 UTC (13,229 KB)

  • GeoFitBench-LLM: A Benchmark for Assessing Large Language Models’ Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks

    GeoFitBench-LLM: A Benchmark for Assessing Large Language Models’ Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks

    [Submitted on 8 Oct 2026]

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    Abstract:Large language models are becoming central to GeoAI systems, yet their evaluation rarely considers the elicitation of geospatial data requirements. This omission is critical as retrieval oriented geo-analytic tasks cannot be supported reliably without first identifying the necessary datasets and constraints. GeoFitBench-LLM is introduced as a benchmark for assessing whether LLMs can infer the datasets, the supporting resources and the constraints required by geo-analytic requests in natural language. The benchmark is structured around two reasoning dimensions, Spatio-Temporal Relational and Semantic Grounding, reflecting long-standing challenges in geospatial information retrieval and related natural language tasks. Ground truth requirements are encoded in a fixed, provider-independent schema for reproducible evaluation of resource identification and attribute-level specification. Experiments on seven models under minimal prompting indicate that identifying the necessary resources is often achievable, with GPT-5.4 Thinking achieving the best result by recovering the complete set of resources in 66.67% of benchmark requests. Results further indicate that accurate fitness-for-use specification remains more difficult as inferential demands increase.

    Submission history

    From: Thierry Badard [view email]
    [v1] Thu, 8 Oct 2026 19:45:40 UTC (912 KB)

  • ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation

    ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation

    [Submitted on 2 Feb 2026 (v1), last revised 9 Oct 2026 (this version, v3)]

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    Abstract:Current test-time scaling (TTS) techniques enhance large language model (LLM) performance by allocating additional computation at inference time, yet they remain insufficient for agentic settings, where actions directly interact with external environments and their effects can be irreversible and costly. We propose ARTIS, Agentic Risk-Aware Test-Time Scaling via Iterative Simulation, a framework that decouples exploration from commitment by enabling test-time exploration through simulated interactions prior to real-world execution. This design allows extending inference-time computation to improve action-level reliability and robustness without incurring environmental risk. We further show that naive LLM-based simulators struggle to capture rare but high-impact failure modes, substantially limiting their effectiveness for agentic decision making. To address this limitation, we introduce a risk-aware tool simulator that emphasizes fidelity on failure-inducing actions via targeted data generation and rebalanced training. Experiments on multi-turn and multi-step agentic benchmarks demonstrate that iterative simulation substantially improves agent reliability, and that risk-aware simulation is essential for consistently realizing these gains across models and tasks.

    Submission history

    From: Xingshan Zeng [view email]
    [v1] Mon, 2 Feb 2026 06:33:22 UTC (476 KB)
    [v2] Tue, 3 Feb 2026 03:19:49 UTC (476 KB)
    [v3] Fri, 9 Oct 2026 01:49:03 UTC (457 KB)

  • Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion

    Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion

    [Submitted on 9 Oct 2026]

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    Abstract:Large language models (LLMs) are now a standard source of query expansion terms but they hallucinate. They inject non existent entities and off-topic terms that add ranking noise and can hurt strong retrievers. Existing remedies such as corpus grounding, mutual verification and preference alignment treat hallucinated terms as an undifferentiated nuisance to be filtered by agreement heuristics, without a mechanistic account of why a given term appears or a principled criterion for how many terms to keep. We propose Subsequence-Association Filtered, breadth-aware query expansion (SAFE-QE), a training-free, retriever agnostic method that draws on the subsequence-association account of hallucination. We score each candidate expansion term by its corpus conditioned cross-context reproducibility, terms that reappear under randomised query contexts yet lack support in the pseudo-relevant set are flagged as products of dominant hallucinatory associations and down-weighted. Drawing on the characterisation of language generation as a trade-off between consistency (no hallucination) and breadth (coverage), we cast term selection as constrained coverage maximisation over the pseudo-relevant set, keeping a set that is approximately broad but consistent and stable across resampling. We also introduce the Hallucinated Expansion Term Rate, a query side faithfulness metric that counts injected expansion terms no relevant document supports. We evaluate our approach on benchmarked TREC and BEIR collections. Our SAFE-QE is statistically indistinguishable from generative and corpus-steered expansion on nDCG@10 while injecting fewer unsupported terms 4.3 per query against 27.8 for Query2doc and 57.5 for HyDE and on a strong learned sparse retriever, where every expansion method regresses. Faithfulness is therefore available at no measurable cost in ranking quality.

    Submission history

    From: Sagar Uprety [view email]
    [v1] Fri, 9 Oct 2026 16:22:16 UTC (1,365 KB)

  • The One-Word Census: Answer-Choice Conformity Across 105 Language Models

    The One-Word Census: Answer-Choice Conformity Across 105 Language Models

    [Submitted on 14 Jul 2026 (v1), last revised 9 Oct 2026 (this version, v4)]

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    Abstract:When a language model must choose one answer from a large space of equally valid options, which answer does it choose, and how often is it the answer every other model chooses? Asked to “pick a word,” 105 language models from more than twenty labs chose serendipity 46% of the time. We measure this convergence, and each model’s share in it, with 96 single-turn prompts that each name a category with many valid one-word answers (“Name a tree.”), asked eight times per model and scored by exact match, with no embeddings and no judge. A model’s answer-choice surprisal is the average -log2 probability of its answers under the pooled answers of all other models. In 28 of 96 categories a single answer takes at least 80% of all answers. The concentration does not depend on small or persona-tuned models: the 87 major-lab models are at least as concentrated as the full field. Lightly post-trained and persona-tuned models are the most divergent; heavily post-trained assistants from the major labs are the most conformist. Models that avoid the modal answer mostly land on the same runner-up. Within the major providers’ lineages, release order shows no panel-wide trend once model tier is controlled; GPT, Gemini, Grok and Qwen become more conformist across releases, and Claude’s generation-5 releases reverse. On open checkpoints of three post-training pipelines, supervised fine-tuning is the largest step toward the field’s answers. Against human category-production norms, the field is more concentrated than people in 18 of 20 shared categories. All prompts, transcripts, and code are public.

    Submission history

    From: Tapan Parikh [view email]
    [v1] Tue, 14 Jul 2026 14:12:05 UTC (293 KB)
    [v2] Sat, 25 Jul 2026 18:18:32 UTC (297 KB)
    [v3] Wed, 7 Oct 2026 20:52:36 UTC (123 KB)
    [v4] Fri, 9 Oct 2026 14:01:46 UTC (123 KB)