News / #reasoning Tag Reasoning 500 articles archived under #reasoning · RSS Sign in to follow arXiv — NLP / Computation & Language research 24d ago Debate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge Graph arXiv:2607.17266v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing. However, LLMs often suffer from hallucinations and lack of relevant knowledge when dealing with question answering (QA) tasks.… 5 arXiv — NLP / Computation & Language research 24d ago Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration arXiv:2607.17531v1 Announce Type: new Abstract: Test-time collaboration, including self-consistency, best-of-N selection, critic models, and verifier pipelines, is often credited with broadly improving LLM reasoning, yet its gains are uneven and sometimes negative. We ask when… 6 arXiv — NLP / Computation & Language research 24d ago C$^2$KV: Compressed and Composable KV Cache Reuse for Efficient LLM Inference arXiv:2607.17715v1 Announce Type: new Abstract: Long-context inference is central to modern large language model (LLM) applications such as retrieval-augmented generation and multi-document reasoning. To mitigate the growing inference cost, recent work has explored key-value… 20 arXiv — NLP / Computation & Language research 24d ago When a Name Is Not a Name: A Benchmark Dataset and Distilled Reasoning for Culturally Entangled Bangla Homographs in Low-Resource LLMs arXiv:2607.17828v1 Announce Type: new Abstract: Many Bangla words are at once personal names and culturally loaded common nouns, "Maya" is both a girl's name and a word for affectionate compassion. Choosing the right reading demands cultural knowledge that is scarce in the… 28 arXiv — NLP / Computation & Language research 24d ago PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning arXiv:2607.18199v1 Announce Type: new Abstract: Not all training samples contribute equally to large language model fine-tuning. Selecting informative training samples can reduce the computational cost while preserving downstream performance. Many existing data selection methods… 25 arXiv — NLP / Computation & Language research 24d ago ColGraphRAG: Late-Interaction Evidence Retrieval for Multimodal GraphRAG arXiv:2607.16208v1 Announce Type: cross Abstract: Graph-grounded multimodal question answering organizes text, tables, and images in a structured evidence graph, yet end-to-end accuracy depends on which multimodal assets are ranked highly enough to enter downstream reasoning;… 6 arXiv — NLP / Computation & Language research 24d ago Training Continuous Chain of Thought Models: A Tale of Two Regimes arXiv:2607.16972v1 Announce Type: cross Abstract: Continuous Chain-of-Thought methods replace verbose reasoning traces with a short sequence of dense latent representations. Earlier continuous CoT methods indirectly supervise the latent representations such that its final state… 29 arXiv — NLP / Computation & Language research 24d ago Thinking in Video: Can Video Generators Really Reason About the Real World? arXiv:2607.17523v1 Announce Type: cross Abstract: Recent advances in world models and video generation have given rise to an emerging reasoning paradigm that leverages video generative models to simulate, predict, and reason about real-world dynamics. We redefine this paradigm… 25 arXiv — NLP / Computation & Language research 24d ago MADA-RL: Multi-Agent Debate-Aware Reinforcement Learning for Parameter-Efficient Reasoning in Compact Models arXiv:2607.18006v1 Announce Type: cross Abstract: Large language models achieve strong reasoning performance, but often at prohibitive training cost - a challenge that is especially acute for compact models ($\leq 4 \, \mathrm{B}$ parameters) trained under limited budgets. We… 15 Hugging Face Daily Papers research 24d ago Distilled Reinforcement Learning for LLM Post-training Abstract Large language model (LLM) post-training is essential for improving reasoning, adaptation, and alignment. Existing methods mainly follow two paradigms: reinforcement learning (RL) and on-policy distillation (OPD). However, RL relies on coarse-grained outcome… 30 LangChain releases dev-tools 24d ago langchain-core==1.5.0 Changes since langchain-core==1.4.9 release(core): 1.5.0 ( #38978 ) feat(core): add reasoning_effort as a standard chat model parameter ( #38887 ) chore: bump soupsieve from 2.8 to 2.8.4 in /libs/core ( #38750 ) chore: bump mistune from 3.2.1 to 3.3.0 in /libs/core ( #38783 ) 30 Hugging Face Daily Papers research 24d ago RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model Abstract We present RynnBrain 1.1, a family of embodied foundation models spanning 2B, 9B, and 122B-A10B scales. Trained with a unified spatio-temporal and physically grounded framework, RynnBrain 1.1 supports embodied perception, spatial reasoning, localization, and planning.… 30 Hugging Face Daily Papers research 24d ago SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning Abstract We introduce Self-Verified Reasoner (SVR-R1), a multi-turn RL framework that turns a model's own verification into a learning signal for multimodal reasoning. For each query, the model proposes an answer using the same weights, and issues a binary self-verdict (Yes/No).… 7 Hugging Face Daily Papers research 25d ago REBASE: Reference-Background Subspace Elimination for Training-Free In-Context Segmentation Abstract Training-free in-context segmentation enables new object categories to be introduced at inference time from a single annotated reference image, eliminating the retraining and memory overhead of class-incremental learning. Recent approaches achieve this by combining… 37 Hugging Face Daily Papers research 25d ago Agon: Competitive Cross-Model RL with Implicit Rival Grading of Reasoning Abstract Reinforcement learning from verifiable rewards (e.g. GRPO) is the engine behind today's reasoning models, yet it grades only the final answer. On hard problems this trains models to write more rather than to think better, since the trace itself is never graded and no… 15 Hugging Face Daily Papers research 25d ago S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation Abstract We present S1-Omni, a unified multimodal reasoning model for scientific understanding, prediction, and generation. AI for Science (AI4S) has advanced significantly through domain-specific models, tool-augmented LLMs, and scientific language models. However, model… 15 arXiv — Machine Learning research 25d ago Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling arXiv:2607.15313v1 Announce Type: new Abstract: The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities. This position paper argues that applying this probabilistic paradigm to generic quantum circuit synthesis is a directional… 4 arXiv — Machine Learning research 25d ago Hard Rules, Soft Preferences: Bridging Reasoning, Learning, and Optimization for Personalized Packing Checklist Generation arXiv:2607.15562v1 Announce Type: new Abstract: Packing for air travel is recurring and error-prone: the checklist must be personal and context-aware, yet feasible under safety rules, item dependencies, and luggage limits. Existing packing assistants are template-driven and… 17 arXiv — NLP / Computation & Language research 25d ago Understanding Reasoning from Pretraining to Post-Training arXiv:2607.16097v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is largely studied in isolation from the pretraining that precedes it. As a result, two… 20 arXiv — Machine Learning research 25d ago Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal arXiv:2605.26772v1 Announce Type: cross Abstract: Large reasoning models (LRMs) generate chain-of-thought (CoT) traces before producing final outputs, introducing a dynamic internal state that may complicate control mechanisms such as refusal. Unlike instruction-tuned LLMs,… 26 arXiv — NLP / Computation & Language research 25d ago Hidden in Thought: Transferable Chain-of-Thought Artifacts Induce Harmful Behavior arXiv:2607.15286v1 Announce Type: cross Abstract: We investigate whether harmful chain-of-thought (CoT) traces from compromised language models can transfer unsafe behaviour and be distilled into reusable jailbreak attacks. Using an emergent-misalignment organism and a… 35 arXiv — NLP / Computation & Language research 25d ago Verbalizable Representations Form a Global Workspace in Language Models arXiv:2607.15495v1 Announce Type: new Abstract: Out of everything the human brain processes, only a small fraction is consciously accessible, in the sense of being available for verbal report, deliberate control, and flexible reasoning. In this paper, we present evidence that an… 16 arXiv — NLP / Computation & Language research 25d ago Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning arXiv:2607.15736v1 Announce Type: new Abstract: Large reasoning models often solve problems through long chain-of-thought (CoT) traces, yet much of this computation is spent on redundant derivations, repeated self-verification, and detours that do not improve the final answer.… 9 arXiv — NLP / Computation & Language research 25d ago Frontier AI performance across the business disciplines: a case-grounded benchmark of knowledge work and analytical reasoning arXiv:2607.16057v1 Announce Type: new Abstract: Large language models (LLMs) are improving rapidly as reflected in benchmark scores, yet these AI benchmarks largely test capabilities such as factual recall, narrow question answering, mathematical problem-solving, and coding and… 14 arXiv — NLP / Computation & Language research 25d ago Frontier Language Models Struggle to Copy: Text Can Be Better Viewed in 2D arXiv:2607.16072v1 Announce Type: new Abstract: While large language models (LLMs) can solve advanced reasoning problems in seconds, we show that even frontier models fail to perform a much simpler operation: exactly copying an input string that lies well within their context… 27 arXiv — NLP / Computation & Language research 25d ago From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence arXiv:2605.06006v2 Announce Type: replace Abstract: Fact-checking articles encode rich supporting evidence and reasoning, yet this evidence remains largely inaccessible to automated verification systems due to unstructured presentation. We introduce PrimeFacts, a methodology and… 8 arXiv — NLP / Computation & Language research 25d ago Length Penalties Make Chain-of-Thought Less Monitorable arXiv:2607.09786v2 Announce Type: replace-cross Abstract: Length-penalized reinforcement learning can shorten chain-of-thought reasoning while hiding an influence that drives the model's answer. In our experiments, training with length penalties does not stop misleading hints… 16 Hugging Face Daily Papers research 25d ago Cura 1T: Specialized Model for Agentic Healthcare Abstract Healthcare spans high-stakes communication, expert reasoning, and workflow execution, yet specialized LLMs that cover these use cases together remain limited. A healthcare model must handle patient consultation, clinical reasoning over text and images, interactive… 20 Hugging Face Daily Papers research 25d ago Audio-Visual Flamingo: Open Audio-Visual Intelligence for Long and Complex Videos Abstract We present Audio-Visual Flamingo (AV-Flamingo), a fully open state-of-the-art audio-visual large language model (AV-LLM) for joint understanding and reasoning over audio, images, and long-form videos. Unlike prior AV-LLMs that primarily focus on short clips, AV-Flamingo… 9 Hugging Face Daily Papers research 25d ago RecGPT-V3 Technical Report Abstract Large language models (LLMs) are transforming recommender systems from matching co-occurrence patterns in historical behavior toward reasoning about the intent that drives it. RecGPT-V1 pioneered this paradigm on Taobao by centering user understanding, and RecGPT-V2… 33 Hugging Face Daily Papers research 25d ago Understanding Reasoning from Pretraining to Post-Training Abstract Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is largely studied in isolation from the pretraining that precedes it. As a result, two basic questions remain open: (1) how do… 38 r/LocalLLaMA community 27d ago [Model] catmind-1.2b Everyone loves cats, even LLMs. If you are like me and can't resist the urge of petting a cat that approaches you, you will LOVE catmind. catmind-1.2b is a cat-thinking model: instead of thinking about your query, it uses it's thinking block to tell you a story about cats. Yes,… 10 Ahead of AI (Sebastian Raschka) research 27d ago Controlling Reasoning Effort in LLMs How LLMs Learn Low-, Medium-, and High-Effort Reasoning Modes 36 Hugging Face Daily Papers research 27d ago On Locality and Length Generalization in Visual Reasoning Abstract A striking feature of the human visual system is that it ingests visual information through a series of local foveated glimpses, rather than a single global computation. This makes human vision distinctly different from most popular computer vision models in use today,… 23 Hugging Face Daily Papers research 28d ago Hierarchical Denoising For Multi-Step Visual Reasoning Abstract Video models are evolving into vision foundation models, yet they still lack human-like multi-step reasoning. Streaming autoregressive diffusion models are efficient but limited in reasoning, while bidirectional diffusion enables global revision with high inference… 23 Hugging Face Daily Papers research 28d ago RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination Abstract Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagination. Unlike vision-language models… 18 Hugging Face Daily Papers research 28d ago Spectral Rewiring for Exploration, Purification, and Model Merging Abstract Reinforcement learning has become a standard post-training recipe for large language models, but dense full-parameter updates create two deployment-relevant bottlenecks: suppressed reasoning performance, often reflected by premature saturation of test-time scaling, and… 35 Hugging Face Daily Papers research 28d ago UniVR: Thinking in Visual Space for Unified Visual Reasoning Abstract Learning broad world knowledge directly from raw visual data is a fundamental capability of intelligence. We introduce UniVR, the first investigation into simultaneously learning complex reasoning, fine-grained physical dynamics, and long-term planning from pure visual… 30 arXiv — Machine Learning research 28d ago Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards arXiv:2607.14506v1 Announce Type: new Abstract: While reinforcement learning with verifiable rewards (RLVR) is widely used to improve the reasoning capabilities of large language models (LLMs), the generalizability of the resulting models remains poorly understood. In this work,… 30 arXiv — Machine Learning research 28d ago GAttNHP: Group Attention Neural Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs arXiv:2607.14733v1 Announce Type: new Abstract: Temporal Knowledge Graphs (TKGs) record how facts evolve over time, but forecasting future events on a TKG remains difficult for three reasons: (i) long-range temporal dependencies are hard to encode; (ii) events on different… 14 arXiv — NLP / Computation & Language research 28d ago Leveraging Instruction Tuning and Merging for Reasoning Model Adaptation arXiv:2607.14895v1 Announce Type: cross Abstract: Reasoning language models (RLMs) have demonstrated impressive performance in domains such as mathematics and coding. These domains permit reliable verification of model outputs, which is important for enabling the reinforcement… 35 arXiv — Machine Learning research 28d ago Enhancing Small Language Models Reasoning through Knowledge Graph Grounding arXiv:2607.14149v1 Announce Type: cross Abstract: Although large language models (LLMs) have set benchmarks for zero-shot reasoning, their deployment remains cost-prohibitive and environmentally taxing. Small Language Models (SLMs) offer a sustainable alternative, but prone to… 24 arXiv — NLP / Computation & Language research 28d ago Just Keep Prompting: Evaluating Repetitive Socratic Prompting in VLMs arXiv:2607.14099v1 Announce Type: new Abstract: Deploying Vision-Language Models (VLMs) in real-world settings requires not only strong visual reasoning but also stability under sustained conversational pressure. We introduce Just Keep Prompting (JKP), a multi-turn evaluation… 10 arXiv — NLP / Computation & Language research 28d ago CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning arXiv:2607.14114v1 Announce Type: new Abstract: Graph learning under distribution shift presents a persistent challenge, where models adapt to new graphs with limited or even no supervision. Recent graph--LLM approaches move toward label-efficient prediction by linearizing… 12 arXiv — NLP / Computation & Language research 28d ago Heterogeneous Element-Aware Cross-Version Differencing of Scientific Documents via Layout-Aware Alignment and Structure-Aware Reasoning arXiv:2607.14117v1 Announce Type: new Abstract: Cross-version differencing of scientific documents is essential in scholarly publishing and technical documentation, but remains challenging because scientific documents are page-structured artifacts containing heterogeneous… 17 arXiv — NLP / Computation & Language research 28d ago Implicit Reasoning Steering via Concept Chaining arXiv:2607.14242v1 Announce Type: new Abstract: Large language models often appear to reason reliably, yet on many questions repeated sampling yields both correct and incorrect answers, revealing an underlying fragility in how final decisions are formed. We study whether this… 30 arXiv — NLP / Computation & Language research 28d ago Multi-Head Latent Control: A Unified Interface for LLM Agent Decision Making arXiv:2607.14277v1 Announce Type: new Abstract: Large language models are increasingly deployed as agents, but reliable agentic behavior requires more than next-token prediction. At inference time, it is preferred that an agent can decide whether to proceed with its current… 26 arXiv — NLP / Computation & Language research 28d ago HABIB_TAZ at SemEval-2026 Task 11: Disentangling Formal Logic from Content via Synthetic Training and Multi-Objective Optimization arXiv:2607.14349v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel in many general NLP tasks, their formal reasoning capabilities are often compromised by content effects, demonstrating a measurable bias towards real-world plausibility. In this paper, we… 12 arXiv — NLP / Computation & Language research 28d ago Answer-Conditioned Chains of Thought Degrade Verifiable-Reasoning Distillation in Large Language Models arXiv:2607.14552v1 Announce Type: new Abstract: A standard recipe for distilling the reasoning ability of large language models (LLMs) is to sample chains of thought from the model, keep those that reach the correct final answer, and fine-tune on the survivors. When sampling… 21 arXiv — NLP / Computation & Language research 28d ago MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA arXiv:2607.14561v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong reasoning performance, but their tendency to hallucinate limits their reliability in knowledge-intensive tasks requiring up-to-date and grounded information. Combining knowledge… 33 Page 10 of 10 · 500 articles ← Newer
