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MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering
The expansion of large language models beyond assistance roles into autonomous system development represents one of the most discussed shifts in contemporary artificial intelligence. As these models grow in capability, a natural p...
Lean Graph Search for Formal Theorem Proving
Formal theorem proving sits at the intersection of logic, mathematics, and machine learning. In systems like Lean 4, provers must construct machine-checkable proofs by drawing on large formalized libraries of mathematical results....
Designing Transformers for Continuous Diffusion Language Models
Continuous diffusion language models (CDLMs) offer an alternative approach to parallel language generation; however, most existing studies directly use transformer architectures from either causal large language models (LLMs) or D...
KnowGPO: Knowledge-Grouped Policy Optimization for Search Agents
Deep search agents solve knowledge-intensive tasks through long-horizon multi-turn interactions. Assigning credit to individual search decisions is fundamentally difficult. Outcome-based reinforcement learning provides only a sing...
Training Reasoning Models to Generate Diverse Scientific Ideas
One of the first steps of a research pipeline is generating ideas. Large Language Models LLMs are increasingly used to automate this step, but most of this work optimizes the quality of individual ideas while overlooking diversity...
AVC-Reward: Event-Centric Audio-Video Consistency Evaluation
Joint audio-video generation models can produce scenes with multi-event composition, multi-layer audio mixture, and asymmetric cross-modal relations. However, existing evaluation protocols largely reuse metrics from earlier simple...
Twice Upon a Time: Complementary Representations for Time-Series Reasoning
The Problem With Single-View Time-Series Language Models Time Series Language Models (TSLMs) promise to bring the reasoning power of large language models to temporal data. They can answer questions, forecast future behavior, and...
Learning to Steer LLM Agents and Multi-Agent Systems
Large language model agents and multi-agent systems increasingly tackle complex tasks through long-horizon tool use and inter-agent collaboration. As execution unfolds, these systems are prone to deviating from the overall goal, a...
A tool to compare LLM benchmarks
Developers choice of LLM for a given task often hinges on which model excels in the relevant capability set. Philipp Dubach built Model Atlas with Codex to surface those differences, creating a tool that surfaces how published ben...
LLMs are not AGM (though some have internal monologue)
Leading US LLM vendors consistently frame their roadmaps around achieving artificial general intelligence, positioning themselves as the first to cross that threshold. The narrative suggests that once a model reaches this state, i...