Despite the widespread adoption of text embedding models, selecting the optimal model for a specific target corpus remains challenging due to the lack of task-specific labels. While task-agnostic evaluation offers a promising solution by relying on unlabeled data, existing approaches based on kernel estimators or Gaussian mixtures fail to model high-dimensional distributions effectively, resulting in unstable rankings. To address this limitation, we propose FLARE (Flow-based Label-free Assessment of Representation Embeddings), which employs normalizing flows to estimate information sufficiency in high-dimensional spaces. By learning invertible transformations, flows enable exact density estimation while mitigating the instability inherent in distance-based methods. We provide theoretical guarantees showing that our estimation error depends on the data’s intrinsic structure rather than its raw dimensionality. Experiments across 11 datasets demonstrate that FLARE achieves a strong Spearman’s ρ (up to 0.90) with supervised benchmarks, remaining robust even for high-dimensional embeddings (d ≥ 3,584).
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Parallel thinking offers a promising avenue for scaling test-time compute in Large Language Models (LLMs), enabling them to explore diverse solution paths simultaneously before aggregating them into a final answer. However, coordinating the exploration and aggregation stages remains challenging, as simple aggregation techniques often incur information loss, failing to preserve the subtle, decision-relevant signals generated during exploration. To overcome this, we propose Rhombus, a parallel thinking framework that explicitly incentivizes coordination between components via end-to-end reinforcement learning. Rhombus employs multiple parallel Proposers to generate compact, decision-focused reasoning cues and a central Synthesizer to integrate them into final predictions, utilizing co-training under a shared task reward to align their interaction. Across challenging mathematical reasoning benchmarks, Rhombus improves accuracy by 6.0% over long chain-of-thought baselines while reducing wall-clock latency by 39.4% under matched token budgets. Our work demonstrates that explicit communication optimization is essential for realizing the accuracy and efficiency gains of parallel reasoning.
Standardized benchmarks have become the dominant metric for measuring progress in large language models, yet their validity is increasingly compromised by data contamination and the unclear relationship between benchmark scores and genuine language understanding. We introduce Gaperon, a suite of fully open bilingual (French-English) language models designed as an experimental testbed to investigate evaluation dynamics under realistic training conditions. Our study makes three core contributions. First, we demonstrate mismatches between benchmark performance and generation quality: models that excel on benchmarks may underperform in qualitative text generation, and vice versa. Second, through our deliberately contaminated Gaperon-Garlic variant, we show that competitive benchmark scores can be recovered via late-stage contamination with only moderate degradation of generation quality, and surprisingly, such contamination also improves performance on held-out benchmarks. Third, we provide empirical evidence that widely used neural quality filters, particularly those trained to favor instructional or educational content, amplify benchmark contamination in pretraining corpora, with the DCLM classifier systematically ranking benchmark samples in the top-5 percentiles of samples. We release all models, data mixtures, checkpoints, and evaluation code to support reproducibility and further investigation.
Curiosity serves as a fundamental construct in human cognition.Inspired by curiosity, reinforcement learning with intrinsic rewards for large language models (LLMs) has shown substantial potential.However, it remains unclear whether existing curiosity-driven methods genuinely reflect curiosity-like behaviors in LLMs, and to what extent psychological notions of curiosity can be transferred to these models. In this work, we propose a psychology-inspired framework to evaluate and leverage curiosity in LLMs.We adapt the Five-Dimensional Curiosity scale Revised (5DCR) to LLMs and combine questionnaire-based self reports with behavioral study.We find that although LLMs can exhibit curiosity-like behavioral patterns resembling those of humans, such patterns do not reflect an intrinsic trait of curiosity.Building on this insight, we design a curiosity-driven thinking pipeline to examine the functional role of human-like curious behaviors. Experiments show that instructing LLMs to emulate curious strategies leads to better performance on selected downstream tasks, indicating that mimicking curious behaviors holds promise for reasoning enhancement.
SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering
PDF ↗Multi-hop Question Answering (MHQA) aims to answer questions that require multi-step reasoning. The complexity of user queries, coupled with potential knowledge deficiencies in Large Language Models (LLMs), gives rise to two pivotal challenges that underpin the performance on this task: the correct identification of the reasoning path and the accurate retrieval of essential knowledge. Existing approaches primarily rely on prompt-based methods to generate reasoning paths, which are further combined with traditional sparse or dense retrieval to produce the final answer. However, the generation of reasoning paths commonly lacks effective control over the generative process, thus leading the reasoning astray. Meanwhile, the retrieval methods over-rely on knowledge matching or similarity scores rather than evaluating the practical utility of the information, resulting in retrieving homogeneous or non-useful information. Therefore, we propose a Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator framework named SEARCH-R. Specifically, SEARCH-R trains an end-to-end reasoning path navigator, which is able to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model. Moreover, a novel dependency tree-based retrieval is designed to evaluate the informational contribution of the document quantitatively. Extensive experiments on three challenging multi-hop datasets validate the effectiveness of the proposed framework. The code and dataset are available at: https://github.com/Applied-Machine-Learning-Lab/ACL2026_SEARCH-R.
While Large Language Models (LLMs) demonstrate remarkable zero-shot generalization, adapting them to downstream tasks or shifting data distributions often requires continual fine-tuning—a process prone to catastrophic forgetting and limited knowledge transfer. This challenge is especially pronounced in online Incremental Learning (IL) settings, where task boundaries are blurred, and data arrives in a non-stationary stream. To address these issues, we propose GROLE (Group Relative Optimization for LoRA Experts), a novel approach that incrementally constructs a pool of frozen, task-specific Low-Rank Adaptation (LoRA) experts. At its core, GROLE employs a lightweight, instance-level expert selector optimized through a group relative reinforcement learning objective, which dynamically combines relevant experts to maximize adaptability without compromising stability. Extensive experiments across diverse incremental learning benchmarks show that GROLE consistently outperforms state-of-the-art methods, particularly in task-free and blurred-boundary settings, achieving an optimal balance between plasticity and robustness.
Constructing Knowledge Graphs (KGs) from unstructured text provides a structured framework for knowledge representation and reasoning, yet current LLM-based approaches struggle with a fundamental trade-off: factual coverage often leads to relational fragmentation, while premature consolidation causes information loss. To address this, we propose SocraticKG, an automated KG construction method that introduces question-answer pairs as a structured intermediate representation to systematically unfold document-level semantics prior to triple extraction. By employing 5W1H-guided QA expansion, SocraticKG captures contextual dependencies and implicit relational links typically lost in direct KG extraction pipelines, providing explicit grounding in the source document that helps mitigate implicit reasoning errors. Evaluation on the MINE benchmark demonstrates that our approach effectively addresses the coverage-connectivity trade-off, achieving superior factual retention while maintaining high structural cohesion even as extracted knowledge volume substantially expands. These results highlight that QA-mediated semantic scaffolding plays a critical role in structuring semantics prior to KG extraction, enabling more coherent and reliable graph construction in subsequent stages.
Group Relative Policy Optimization (GRPO) has demonstrated wide adoption in the post-training of Large Language Models (LLMs). In GRPO, prompts are answered by the model and preferred behaviour is learnt via reinforcement learning. Owing to the small communication volume, GRPO is inherently suitable for decentralised training as the prompts can be concurrently answered by multiple nodes and these completions are exchanged in the form of strings. In this work, we explore the robustness of decentralised GRPO by presenting the first adversarial attacks and countermeasures. We present a diverse set of attacks where malicious nodes poison benign models by sharing their poisoned completions. We demonstrate these attacks on math and coding tasks and show that an adversary can achieve attack success rates of up to (100%) in as few as 50 iterations. Moreover, to mitigate the attacks, we propose two defense mechanisms that check logit probabilities of completions or utilize an LLM judge to filter completions. The defenses prevent all but the DoS attack that causes unnecessarily lengthy but conceptually correct completions. The code of both attacks and defenses can be found at: https://github.com/gensyn-ai/HTTT.
Multimodal reasoning aims to enhance the capabilities of MLLMs by incorporating intermediate reasoning steps before reaching the final answer. It has evolved from text-only reasoning to the integration of visual information, enabling the thought process to be conveyed through both images and text. Despite its effectiveness, current multimodal reasoning methods depend on explicit reasoning steps that require labor-intensive vision-text annotations and inherently introduce significant inference latency. To address these issues, we introduce multimodal latent reasoning with the advantages of multimodal representation, reduced annotation, and inference efficiency. To facilicate it, we propose Interleaved Vision-Text Latent Reasoning (IVT-LR), which injects both visual and textual information in the reasoning process within the latent space. Specifically, IVT-LR represents each reasoning step by combining two implicit parts: latent text (the hidden states from the previous step) and latent vision (a set of selected image embeddings). We further introduce a progressive multi-stage training strategy to enable MLLMs to perform the above multimodal latent reasoning steps. Experiments on M^3CoT and ScienceQA demonstrate that our IVT-LR method achieves an average performance increase of 5.45% in accuracy, while simultaneously achieving a speed increase of over 5 times compared to existing approaches. The code are attached in the supplementary file for the review.
Reference-free image–to–text evaluators are now standard for scoring image–caption alignment, yet it is unclear whether they respect semantic invariances. We present an invariance probe on five popular evaluators (CLIPScore, PAC-S, UMIC, FLEUR, and a deterministic LLM judge) under semantics-preserving perturbations along three axes: spatial (flips, context-preserving repositioning, light rotations), object (scale, category), and socio-linguistic framing (cultural/economic adjectives with neutral and length-matched controls). Across curated slices of three detection datasets and three caption evaluation suites, we find consistent non-semantic sensitivities: benign spatial edits and simple phrasing changes shift scores by (≈)6–9% on average, and for systems separated by just 0.7% these shifts can cause ranking flips in upto (∼)37% of cases, particularly under spatial changes. A small human study also supports this finding and confirms that annotators generally judge perturbed pairs as equally correct, so these shifts reflect metric behavior rather than semantic change. We further propose invariance-calibrated scoring, a post-hoc adjustment that roughly halves median absolute sensitivity while retaining correlation with learned caption evaluators.
While large language models (LLMs) excel at static scientific reasoning, they struggle to model the temporal structure of dynamic physical processes. We present EvoMD-LLM (Evolutionary Molecular Dynamics Large Language Model), a framework that reformulates species-level molecular dynamics as a symbolic temporal language modeling problem. Reactive MD trajectories are discretized into sequences of molecular events, where each token represents a chemical species augmented with its persistence duration, enabling standard autoregressive LLMs to learn compositional evolution over time through efficient fine-tuning. A key component of EvoMD-LLM is temporal scaffolding, which treats event duration as an explicit linguistic token and serves as a structured inductive bias, significantly reducing invalid or hallucinated molecular outputs compared to conventional sequence modeling approaches. We evaluate EvoMD-LLM on multiple temporal prediction tasks, achieving up to 66.14% accuracy and consistently outperforming sequential neural networks and language-based baselines. Beyond quantitative improvements, we qualitatively observe that the model can generate plausible physical interpretations of reaction dynamics, despite not being explicitly trained for explanation. These results demonstrate that symbolic temporal language modeling provides an effective framework for grounding LLMs in dynamic physical simulations.
Multi-tenant Model-as-a-Service (MaaS) LLM serving must maintain stringent quality of service (QoS) despite heterogeneous requests competing for constrained GPU resources. In practice, MaaS workloads exhibit non-stationarity across multiple time scales, including request bursts, request-composition drift, and persistent workload shifts. Because workloads change across multiple time scales, existing request schedulers often rely on a single fixed policy (e.g., First-Come-First-Served, FCFS) that remains unchanged at runtime, which can lead to unstable QoS. We propose H-MAS, a hierarchical multi-agent scheduler that operates in a layered closed loop: a perception/prediction layer infers lightweight request attributes and cost signals; a feedback layer summarizes runtime metrics into short- and long-horizon QoS states; a hierarchical control layer updates the active scheduling policy over longer horizons and tunes execution parameters over shorter horizons; and an execution layer applies these decisions during inference. Experiments with load scaling and Azure-trace replays show that H-MAS achieves 1.2\times–3.0\times higher Goodput than SGLang and vLLM, and maintains more stable QoS under workload drift, diverse request lengths and heterogeneous SLO targets.
Short-form news videos increasingly shape public perception through strategic framing, yet existing verification methods largely overlook the communicative intent underlying such content. By emphasizing surface semantics, current models struggle to separate stylistic presentation from factual evidence, which leads to shortcut learning and brittle generalization. To address this limitation, we propose the Origin–Objective–Means (OOM) framework, a theory-grounded representation of communicative intent that captures creator stance, audience need activation, and communication strategy. We validate OOM through large-scale human annotation, revealing distinct and consistent lexical and structural patterns across intent dimensions. Building on this representation, we operationalize intent as an explicit semantic condition rather than a prediction target. Concretely, we introduce Intent-Guided Prompting (IGP) to condition LLM reasoning and intent-conditioned multimodal detection framework (ICMD), which injects intent into multimodal detectors via feature-wise modulation. Experiments on FakeSV and FakeTT show that modeling intent as an intermediate condition consistently improves accuracy and robustness across diverse vision–language backbones, while substantially reducing reliance on spurious stylistic correlations.
Improved Policy Optimization for Mixture-of-Experts Models: Importance Sampling and Rewarding from an Expert-Centric Perspective
PDF ↗Reinforcement learning (RL) has demonstrated considerable promise in enhancing large language models. However, its application to Mixture-of-Experts (MoE) architectures is frequently hindered by training instability, primarily stemming from token-level misalignment in expert assignments between current and behavior policies. Existing approaches often oscillate between overly coarse sequence-level importance sampling, which ignores token-specific discrepancies, and restrictive expert-selection constraints that suppress beneficial policy exploration. To bridge this gap, we propose Expert Relative Policy Optimization (ERPO), which introduces expert-level importance sampling. By grouping tokens according to their routing assignments, ERPO mitigates the high variance of token-level importance sampling while overcoming the token-agnostic limitations of sequence-level methods. Furthermore, ERPO leverages this expert-centric granularity to introduce an Expert-Selection Entropy Reward, which dynamically adjusts routing uncertainty based on task-specific feedback. This unique mechanism ensures a rigorous alignment between reward signals and policy updates—a capability inherently unattainable by traditional importance sampling methods. Experimental results demonstrate that ERPO significantly outperforms strong baselines across multiple reasoning tasks, highlighting the efficacy of tailoring RL objectives to the structural inductive biases of MoE models.
CausalGaze: Unveiling Hallucinations via Counterfactual Graph Intervention in Large Language Models
PDF ↗Despite the groundbreaking advancements made by large language models (LLMs), hallucination remains a critical bottleneck for their deployment in high-stakes domains. Existing classification-based methods mainly rely on static and passive signals from internal states, which often captures the noise and spurious correlations, while overlooking the underlying causal mechanisms. To address this limitation, we shift the paradigm from passive observation to active intervention by introducing CausalGaze, a novel hallucination detection framework based on structural causal models (SCMs). CausalGaze models LLMs’ internal states as dynamic causal graphs and employs counterfactual interventions to disentangle causal reasoning paths from incidental noise, thereby enhancing model interpretability. Extensive experiments across four datasets and three widely used LLMs demonstrate the effectiveness of CausalGaze, especially achieving over 5.2% improvement in AUROC on the TruthfulQA dataset compared to state-of-the-art baselines.
Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of Large Language Models (LLMs), yet it often incurs substantial computational costs due to “over-reasoning”—the generation of redundant, verbose, or irrelevant steps. While existing reasoning step evaluators effectively detect logical fallacies and factual errors, our analysis reveals a critical blind spot: they fail to penalize “valid but inefficient” reasoning steps that inflate token usage without contributing to the solution. To systematically diagnose this limitation, we introduce RIV-GSM8K, a diagnostic benchmark injected with five distinct types of inefficiencies, including circular reasoning and excessive decomposition. Diagnostic experiments reveal that state-of-the-art evaluators struggle to distinguish these inefficiencies from necessary reasoning. To address this gap, we propose CAID (Context-Aware Information Density), a training-free metric grounded in information theory that identifies low-utility steps. To validate the metric’s practical utility, we apply it within PACE, a post-hoc compression strategy. Additional control experiments show that the gains of PACE are not explained by trivial pruning: compared with random step removal and PRM-based compression baselines, it preserves accuracy at substantially higher compression rates. Empirical results on GSM8K, StrategyQA, and ARC-Challenge demonstrate that PACE reduces token consumption by 31–53% while maintaining accuracy, confirming that CAID successfully distills informational “froth” from reasoning chains without compromising deductive validity.
SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization
PDF ↗Small language models (SLMs), such as BART, can achieve summarization performance comparable to large language models (LLMs) via distillation. However, existing LLM-based ranking strategies for summary candidates suffer from instability, while classical metrics (e.g., ROUGE) are insufficient to rank high-quality summaries. To address these issues, we introduce SCURank, a framework that enhances summarization by leveraging Summary Content Units (SCUs). Instead of relying on unstable comparisons or surface-level overlap, SCURank evaluates summaries based on the richness and semantic importance of information content. We investigate the effectiveness of SCURank in distilling summaries from multiple diverse LLMs. Experimental results demonstrate that SCURank outperforms traditional metrics and LLM-based ranking methods across evaluation measures and datasets. Furthermore, our findings show that incorporating diverse LLM summaries enhances model abstractiveness and overall distilled model performance, validating the benefits of information-centric ranking in multi-LLM distillation.
Vision-language model (VLM) hallucination is commonly linked to imbalanced allocation of attention across input modalities: system, image and text. However, existing mitigation strategies tend towards an image-centric interpretation of these imbalances, often prioritising increased image attention while giving less consideration to the roles of the other modalities. In this study, we evaluate a more holistic, system-mediated account, which attributes these imbalances to functionally redundant system weights that reduce attention to image and textual inputs. We show that this framework offers a useful empirical perspective on the yes-bias, a common form of hallucination in which VLMs indiscriminately respond ‘yes’. Causally redistributing attention from the system modality to image and textual inputs substantially suppresses this bias, often outperforming existing approaches. We further present evidence suggesting that system-mediated attention imbalances contribute to the yes-bias by encouraging a default reliance on coarse input representations, which are effective for some tasks but ill-suited to others. Taken together, these findings firmly establish system attention as a key factor in VLM hallucination and highlight its potential as a lever for mitigation.
Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias and error accumulation, as early mistakes propagate irreversibly through autoregressive decoding. In this work, we propose DiffCoT, a diffusion-styled CoT framework that reformulates CoT reasoning as an iterative denoising process. DiffCoT integrates diffusion principles at the reasoning-step level via a sliding-window mechanism, enabling unified generation and retrospective correction of intermediate steps while preserving token-level autoregression. To maintain causal consistency, we further introduce a causal diffusion noise schedule that respects the temporal structure of reasoning chains. Extensive experiments on three multi-step CoT reasoning benchmarks across diverse model backbones demonstrate that DiffCoT consistently outperforms existing CoT preference optimization methods, yielding improved robustness and error-correction capability in CoT reasoning.
Chain-of-thought (CoT) often improves multi-step reasoning, but it remains unclear what kind of additional sequential computation longer traces actually enable. We connect CoT to Bennett’s logical depth, separating an answer’s description length from the sequential effort required to derive it, and view a CoT budget of T steps as a qualitative cap on realizable sequential computation. To operationalize realized depth beyond raw length, we introduce Effective Logical Depth (ELD), a deletion-based measure of step necessity under a specified inference interface. Across depth-controlled prefix-sum tasks and GSM8K rationale perturbations, we observe two consistent signatures of a Time-for-Accuracy tradeoff: (i) plateau-to-transition accuracy curves as the budget increases from being below to matching the task’s required depth, and (ii) sparse, position-dependent deletion sensitivity concentrated in early steps for deeper instances. On GSM8K, an Extract interface, where the model reads off the answer from the remaining rationale, remains near-perfect even after prefix deletions, whereas a Repair interface, where the model must re-solve from truncated rationale context, degrades markedly. Moreover, Socratic human rationales are consistently more robust than Main rationales under Repair. These results suggest that longer CoT helps primarily when it enables additional effective sequential computation, and that deletion-based diagnostics can distinguish computational steps from redundant ones.