27 stories tagged with #reasoning, in publish-time order across the WeSearch catalog. Tag pages update as new stories ingest.
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Video Demo: How Does Model Compression Change AI Reasoning?
In this video, I benchmark Mistral-7B-Instruct-v0.2 on an NVIDIA H200 DigitalOcean GPU in three...…
Beyond 80/20: High-Entropy Minority Tokens Drive Effective RL for LLM Reasoning
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful approach to enhancing the reasoning capabilities of Large Language Models (LLMs), while its mechanis…
I put ChatGPT-5.5 vs Gemini 3.1 Pro through 7 impossible tests — and the winner surprised me
We put OpenAI's new GPT-5.5 and Google's Gemini 3.1 Pro through 7 brutal real-world prompts. The winner of this ultimate AI showdown might surprise you…
Phillies reasoning for offensive struggles sounds crazier than it really is
What's wrong in Philly?…
ChatGPT/Gemini can now draw on your screen to help you navigate complex software
SketchVLM: Vision-language models can annotate images to explain thoughts and guide users.…
Why isn’t LLM reasoning done in vector space instead of natural language?
Why don’t LLMs use explicit vector-based reasoning instead of language-based chain-of-thought? What would happen if they did? Most LLM reasoning we see is expressed through languag…
How to build custom reasoning agents with a fraction of the compute
Training AI reasoning models demands resources that most enterprise teams do not have. Engineering teams are often forced to choose between distilling knowledge from large, expensi…
WNBA MVP A'ja Wilson Gives Perfect Reasoning for Wanting More Trophies
Las Vegas Aces All-Star A'ja Wilson is the reigning WNBA MVP, Defensive Player of the Year, and Finals MVP. She's not satisfied.…
Nemotron-3-Nano-Omni-30B-A3B-Reasoning, New model?
It is Audio-Image/vids-Text -> Text Original BF 16 GGUF:…
Does Point Cloud Boost Spatial Reasoning of Large Language Models?
3D Large Language Models (LLMs) leveraging spatial information in point clouds for 3D spatial reasoning attract great attention. Despite some promising results, the role of point c…
NARE: An LLM agent that amortizes reasoning into memory and executable rules
Contribute to starface77/Neuro-Adaptive-Reasoning-Engine development by creating an account on GitHub.…
Do the "*Claude-4.6-Opus-Reasoning-Distilled" really bring something new to the original models?
No offense to the fine-tune model providers, just curious. IMO the original models were already trained on massive amount of high quality data, so why bother with this fine-tune? J…
The Power of Power Law: Asymmetry Enables Compositional Reasoning
Natural language data follows a power-law distribution, with most knowledge and skills appearing at very low frequency. While a common intuition suggests that reweighting or curati…
FormalScience: Scalable Human-in-the-Loop Autoformalisation of Science with Agentic Code Generation in Lean
Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models. In scientific fields such as physics, domain-specifi…
Analytica: Soft Propositional Reasoning for Robust and Scalable LLM-Driven Analysis
Large language model (LLM) agents are increasingly tasked with complex real-world analysis (e.g., in financial forecasting, scientific discovery), yet their reasoning suffers from …
StoryTR: Narrative-Centric Video Temporal Retrieval with Theory of Mind Reasoning
Current video moment retrieval excels at action-centric tasks but struggles with narrative content. Models can see \textit{what is happening} but fail to reason \textit{why it matt…
CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning
Chain-of-Thought (CoT) prompting has emerged as a simple and effective way to elicit step-by-step solutions from large language models (LLMs). However, CoT reasoning can be unstabl…
Constraint-Based Analysis of Reasoning Shortcuts in Neurosymbolic Learning
Neurosymbolic systems can satisfy logical constraints during learning without achieving the intended concept-label correspondence; this is a problem known as reasoning shortcuts. W…
Ulterior Motives: Detecting Misaligned Reasoning in Continuous Thought Models
Chain-of-Thought (CoT) reasoning has emerged as a key technique for eliciting complex reasoning in Large Language Models (LLMs). Although interpretable, its dependence on natural l…
Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning
Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit step-by-step reasoning before …
Expert Evaluation of LLM's Open-Ended Legal Reasoning on the Japanese Bar Exam Writing Task
Large language models (LLMs) have shown strong performance on legal benchmarks, including multiple-choice components of bar exams. However, their capacity for generating open-ended…
PhysNote: Self-Knowledge Notes for Evolvable Physical Reasoning in Vision-Language Model
Vision-Language Models (VLMs) have demonstrated strong performance on textbook-style physics problems, yet they frequently fail when confronted with dynamic real-world scenarios th…
Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus
Multiple myeloma is managed through sequential lines of therapy over years to decades, with each decision depending on cumulative disease history distributed across dozens to hundr…
Beyond the Attention Stability Boundary: Agentic Self-Synthesizing Reasoning Protocols
As LLM agents transition to autonomous digital coworkers, maintaining deterministic goal-directedness in non-linear multi-turn conversations emerged as an architectural bottleneck.…
A systematic evaluation of vision-language models for observational astronomical reasoning tasks
Vision-language models (VLMs) are increasingly proposed as general-purpose tools for scientific data interpretation, yet their reliability on real astronomical observations across …
Structured CoT: Shorter Reasoning with a Grammar File
Going from 3B/7B dense to Nemotron 3 Nano (hybrid Mamba-MoE) for multi-task reasoning — what changes in the fine-tuning playbook? [D]
Following up on something I posted a few days back about fine-tuning for multi-task reasoning. Read a lot since then, and I've moved past the dense 3B vs 7B question — landing on N…