Search: “reasoning”

arXiv:2602.07096v2 Announce Type: replace-cross Abstract: Reliable financial reasoning requires knowing not only how to answer, but also when an answer cannot be justified. In real financial practice,

arXiv:2604.21501v1 Announce Type: new Abstract: Lithology classification in well logs is a fundamental geoscience data mining task that aims to infer rock types from multi dimensional geophysical sequ

arXiv:2601.12499v2 Announce Type: replace Abstract: Despite scaling to massive context windows, Large Language Models (LLMs) struggle with multi-hop reasoning due to inherent position bias, which caus

arXiv:2510.00861v2 Announce Type: replace Abstract: While search-augmented large language models (LLMs) exhibit impressive capabilities, their reliability in complex multi-hop reasoning remains limite

arXiv:2510.08483v2 Announce Type: replace Abstract: Parallel scaling has emerged as a powerful paradigm to enhance reasoning capabilities in large language models (LLMs) by generating multiple Chain-o

arXiv:2604.12506v1 Announce Type: new Abstract: Recent Audio Large Language Models (AudioLLMs) exhibit a striking performance inversion: while excelling at complex reasoning tasks, they consistently u

arXiv:2604.08905v1 Announce Type: new Abstract: Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization

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Nous Research draws its name from the ancient Greek concept of *nous* — the faculty of directly perceiving truth, reason, and divine reality — while its flagship model series, **Hermes**, is named ...

arXiv:2610.12932v1 Announce Type: new Abstract: Search agents extend large reasoning models with multi-round access to external knowledge, yet most existing systems largely conflate retrieving evidenc

arXiv:2610.09639v1 Announce Type: new Abstract: On-policy distillation (OPD) strengthens language-model reasoning, yet whether students acquire new factual knowledge or compositional skill for multi-s

arXiv:2609.13849v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) often struggle with complex mathematical visual reasoning primarily due to a lack of fine-grained perception, c

arXiv:2609.09776v1 Announce Type: cross Abstract: Frontier gains in language-model reasoning come from reinforcement learning on reasoning traces and are concentrated in domains with a cheap, sound ve

arXiv:2608.10976v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models can connect scene understanding, semantic reasoning, and trajectory generation for autonomous driving. However, verb

arXiv:2607.22629v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) produce long, explicit chains of intermediate steps before generating a final answer at inference time. These intermediate

arXiv:2607.24082v1 Announce Type: new Abstract: Recent advances in AI have substantially expanded its cognitive and reasoning capabilities. From the perspective of semantic complexity, the development

arXiv:2606.13061v2 Announce Type: replace Abstract: Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. De

arXiv:2607.08017v1 Announce Type: cross Abstract: Large-Language Models (LLMs) can be prone to flawed and unfaithful reasoning that decoding strategies like Self-Consistency (SC) fail to detect as the

arXiv:2607.08434v1 Announce Type: new Abstract: Current Unified Large Multimodal Models (ULMMs) support interleaved multimodal reasoning through textual reasoning and intermediate visual states, but t

arXiv:2602.14095v2 Announce Type: replace Abstract: Monitoring chain-of-thought (CoT) reasoning is a foundational safety technique for large language model agents; however, this oversight is compromis

arXiv:2601.08758v4 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) reasoning has proven effective in enhancing large language models by encouraging step-by-step intermediate reasoning, a

arXiv:2510.26418v4 Announce Type: replace Abstract: Large Reasoning Models (LRMs) improve task performance through extended inference-time reasoning. Although previous studies suggest that longer reas

Accessing enterprise data is shifting from static reports to dynamic use by autonomous systems. To keep up, organizations must route fragmented data from SaaS, IoT, and legacy sources into secure, sca

arXiv:2605.08975v1 Announce Type: new Abstract: Reasoning-based end-to-end (E2E) autonomous driving has recently emerged as a promising approach to improving the interpretability of driving decisions

arXiv:2603.29957v3 Announce Type: replace-cross Abstract: Recent advances in reasoning Large Language Models (LLMs) have primarily relied on upfront thinking, where reasoning occurs before final answe

To act at the speed of business, AI agents must operate in fast and trusted reasoning loops. They need to “think” by reasoning across both your historical context and your live operational reality. On

arXiv:2604.16451v1 Announce Type: new Abstract: Recent advances in visual-language models (VLMs) have led to significant improvements in a plethora of complex multimodal tasks like image captioning, r

arXiv:2512.07407v2 Announce Type: replace Abstract: Language models frequently produce plausible yet incorrect reasoning traces that are difficult to verify. We investigate fine-tuning models to use P

arXiv:2604.16242v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) typically optimizes for outcome rewards without imposing constraints on intermediate reasoning.

arXiv:2604.10426v1 Announce Type: cross Abstract: Large Language Models (LLMs) struggle with knowledge-intensive tasks due to hallucinations and fragmented reasoning over dispersed information. While

arXiv:2511.22396v2 Announce Type: replace-cross Abstract: Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (

not a remotely reasonable extrapolation. more like desperate attempt to prop up a rapidly declining IPO. BREAKING: Anthropic says 'AI could push US GDP to $44 trillion by 2030' 70% chance of IPO this

GPT-Rosalind is built to support that work: helping researchers reason across scientific literature and biological data, with scientists bringing the expertise and judgment to evaluate the evidence. E

JEFFERIES: “.. the reason credit risk has become more of an issue is that it can no longer be assumed .. that large language models will ever be profitable .. .. in this respect, there is a 2008 real

Investors pattern matching on old movies is going to lead to a recession. There is no network effect here and no reason to think that large language models will be anything other than a brutal commodi

The real reason why Anthropic is so DESPERATE for the Pope to recognize Ai as conscious is because if Ai just a “TOOL” then Anthropic will he LEGALLY “LIABLE” for everything the tool does.🚨👀 Because i

arXiv:2609.36608v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains multi-turn language agents with dense teacher supervision on student-generated responses. However, standard think-

BREAKING: OpenAI postpones 6.1 for safety reasons. If we can take OpenAI at face value, this is quite a decision—and also a sign that no magical solution is in immediate view. Per @sheeraf at NYT “GPT

Joe / @joedaroo: An OpenAI agent security executive discusses the Hugging Face incident, OpenAI's response, sandboxing improvements, alignment, “reasonable paranoia”, and more — Took a minute to write

I've been suspicious of the philosophers hired by Anthropic who specialize in AI ethics for similar reasons that I'm skeptical of those who went into 'bioethics': they form a closed subculture that de

No AI model company is reasonably going to slow down until they have a model that can solve 100 long-standing open math problems We’re working with an independent advisory group of mathematicians to h

One of the reasons AI will change things less quickly than many think is illustrated in this post. OpenAI is working with mathematicians to ensure they release new proofs in a less disruptive way. The

the reason i'm excited about decision models: agentic systems are just good engineering around models. 'build prod, not god' these types of fast, cheap & accurate models are super important for that t

Truman Dickerson / Business Insider: Jensen Huang says AI leaders calling for regulation don't want new legislation, but to be “relieved of the laws we do have” because of “ulterior reasons” — Don't l

Banger paper from Google Research. This one is on how LLM assistants reason about the people in a user's life. People ask assistants for social advice constantly, and the assistant only hears the user

Salesforce Inc. said today at its annual user conference Dreamforce that it has partnered with Nvidia Corp. to train and release Koa, a specialized artificial intelligence model that’s custom built fo