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Зерттеулер1 мин

Rules to Tools: Executable Checks for LLM Agents in Scientific Computing

Scientific coding agents receive equations, boundary conditions, and output requirements in writing, then must assess the programs they revise. Rules to Tools (R2T) supplies prepared executable checks of public scientific requirements. Matched SciCode repair groups share written…

arXiv — cs.AI
Зерттеулер1 мин

Fault-Tolerant Budget Conservation in Distributed Multi-Agent Delegation

Resource limits are becoming an authorization boundary for AI agents that delegate work across concurrent and failure-prone workers. Parent-child allocation constraints, affine objects, and distributed escrow do not by themselves prevent overspend when replies are lost, effects…

arXiv — cs.AI
Зерттеулер1 мин

When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents

Large language model agents rely on external harnesses to pass information between the model and its environment and to recover from execution errors. Yet recovery is usually judged only by average task success. This hides an important tension. The same operation can rescue a…

arXiv — cs.AI
Зерттеулер1 мин

Benchmarking Prompt Optimization of Large Language Models With Chess

Evaluating large language models becomes increasingly challenging as their capabilities advance: benchmarks can saturate, public test sets risk contamination, and assessing harder tasks can require expensive grading or execution infrastructure. These challenges are amplified in…

arXiv — cs.AI
Зерттеулер1 мин

JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces

LLM agents generate intermediate reasoning and actions token by token, making extended interactions slow and computationally expensive. Jev-style models offer fast probabilistic predictions over finite fields, but require those fields to be specified in advance. This requirement…

arXiv — cs.AI
Зерттеулер1 мин

Heavy-Tailed Memory Traces in Long-Horizon Language Agents

Long-horizon language agents increasingly rely on external memory as a frozen world model, yet current memory systems are usually judged only by task success or token cost. We argue that the missing object is the shape of memory use: under finite context and repeated retrieval,…

arXiv — cs.AI
Зерттеулер1 мин

When Do Causal World Models Help Modular LLM Agents

LLM agents increasingly act through modular systems, such as order, payment, inventory, and shipment services, where actions in one module change which transitions are valid in another. Standard world models usually fit observational traces, but this is not the quantity needed…

arXiv — cs.AI
Зерттеулер1 мин

Gradient-Aligned Pair Selection for Personalized Preference Optimization

Personalizing large language models (LLMs) requires aligning generation behavior with user-specific preferences rather than aggregate quality. While Direct Preference Optimization (DPO) provides a stable framework for preference learning, its effectiveness in personalized…

arXiv — cs.AI
Зерттеулер1 мин

Comedic Fool's Gold: Reward Exploits and Countermeasures in Conversational Humor

We investigate automated rewards for training language models in conversational humor, focusing on reward exploits and countermeasures. Two approaches aim to capture understandable surprise and predicted audience amusement. Controlled tests show that an embedding-based surprise…

arXiv — cs.AI
Стартаптар1 мин

Questions: A new resource to empower young entrepreneurs

The book “Disciplined Entrepreneurship” by Bill Aulet, managing director of the Martin Trust Center for MIT Entrepreneurship and the Ethernet Inventors Professor of the Practice at the MIT Sloan School of Management, walks readers through the 24 steps of starting a venture. With…

MIT News — AI
Зерттеулер1 мин

Opus 5.5 almost lost to an open-source model...

Supabase just launched their own eval, and what's crazy is that Opus 5.5 almost lost to an open-source model. But, the point of this benchmark is not to find out which model is smartest. Instead, they drop each agent into a Supabase project and give it actual jobs. Build a…

YouTube — David Ondrej (AI Agents)
Жалпы1 мин

Whatever AI Safety Is, It’s Not This

Asking AI companies to self-regulate is a great way to pretend like you’ve accomplished something.

Wired — Artificial Intelligence
Жалпы1 мин

Top 10 robotics stories of September 2026

September 2026 was a packed month for the robotics industry. Live events, including IMTS, ROSCon, and iROS, brought a flood of developments around automation and AI. This month, our top 10 is filled with new products, company launches, and major changes for some robotics…

The Robot Report