Category
7,777 papers
A computational-theoretical framework is established to provide an account of psychopathology applicable to LLMs and experiments suggest that network-theoretic computations of psychopathology may have emerged in LLMs.
This work introduces the cliff token, a token at which the estimated probability of reaching the correct answer (success probability) drops beyond an adaptive threshold, and introduces a cliff taxonomy of deterministic, uncertain, and sampled-off cliffs, defined by greedy choice and token entropy.
An evaluation framework AgentCL for continual learning in agents is presented, centered on distinguishable task streams and metrics for CL properties, and MemProbe is developed, a probing method that stores interactions, insights, and skills while filtering unreliable experiences during consolidation.
LLM-powered coding agents spend the majority of their token budget reading repository files, yet much of the retrieved code is irrelevant to the task at hand. Existing learned pruners compress this context with a single-objective sequence labeler, collapsing all facets of code relevance into one score and one transitio...
Reach audiences
Advertise in front of researchers, engineers, and readers.
ScaleLogic is introduced, a synthetic logical reasoning framework that offers independent control over two axes of difficulty: the depth of the required proof planning and the expressiveness of the underlying formal system.
It is hypothesized that in long traces, hindsight reveals which steps were unnecessary more readily than which steps would have repaired the solution, and that changing the divergence, enriching or reinjecting the privileged context, and training longer only move OPSD along the same accuracy-length tradeoff.
BALAR (Bayesian Agentic Loop for Active Reasoning), a task-agnostic outer-loop algorithm that requires no fine-tuning and enables multi-turn interaction between an LLM agent and a user, is proposed.
It is found that task type is a stronger predictor of acceptance than tree depth, and chat produces the highest entropy yet the highest acceptance rate, which has direct implications for domain-aware speculation budgets and draft-model selection strategies.
MMORF is presented, a framework for constructing language model-based multi-agent systems (MAS) for multi-objective retrosynthesis planning that achieves strong safety and cost metrics on soft-constraint tasks, frequently Pareto-dominating baseline routes, and outperforming state-of-the-art baselines.
While multi-agent systems (MAS) excel at complex reasoning, they are vulnerable to errors that intermediate agents introduce and downstream agents build upon. Auditing intermediate messages before they propagate requires an explicit standard, yet evaluation rubrics are typically authored by domain experts or written ag...
Advances in large language models (LLMs) are driving a shift toward using reinforcement learning (RL) to train agents from iterative, multi-turn interactions across tasks. However, multi-turn RL remains challenging as rewards are often sparse or delayed, and environments can be stochastic. In this regime, naive traject...
Chain-of-Thought (CoT) empowers Large Language Models (LLMs) to tackle complex problems, but remains constrained by the computational cost and early token commitments in discrete reasoning traces. Recent latent reasoning approaches attempt to optimize efficiency by performing reasoning within continuous hidden states....
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
A Blog post by Liquid AI on Hugging Face
Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking are our most advanced live dialogue models yet, built for natural conversation.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
