Factorized Hypothesis Search for Evidence-to-Taxonomy Retrieval
Large-taxonomy retrieval often assumes that the input already expresses the target concept. In many settings, however, the input is indirect evidence, such as a table cell…
Latest
Ouroboros: A Self-Developing Frontier Coding Agent with Reviewed Core Evolution
We present Ouroboros, a self-developing agent harness whose tools, prompts, context assembly, and core implementation improve through reviewed commits that become the runtime for later work.…
Evo-Bench: Can Language Models Improve Agent Harness?
Large Language Models (LLMs) have driven rapid progress in autonomous agents, yet standard evaluations remain confined to static task solving. An emerging frontier is harness evolution---the…
Intent Speaks Louder: Controllable User Simulation Beyond Response Imitation
User simulators are widely used as scalable environments for training and evaluating interactive assistants. Generating the next user turn is inherently one-to-many: the same profile and…
Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA
Macaron-V1 is an open agent-model family for experiential intelligence: learning from experience in real environments and continuing to learn after deployment. It is organized around two…
SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring
As AI coding agents take on increasingly complex, long-horizon software engineering tasks, existing benchmarks are rapidly saturating and their evaluation quality has come under serious scrutiny:…
Motif 3: Technical Report
We introduce Motif 3, a decoder-only Mixture-of-Experts language model with 314 billion total parameters and 13.2 billion activated per token. Each sparse MoE layer contains 384…
Sci-VBench: Evaluating Knowledge- and Reasoning-Intensive Video Generation in Science Domains
We introduce Sci-VBench, a comprehensive benchmark for evaluating knowledge- and reasoning-intensive video generation across scientific domains. It contains 1,253 expert-annotated examples spanning 60 subjects across four…
RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States
Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges. First, trajectory-indexed utilities grow with the interaction history, thereby dispersing limited feedback over an…
SPOT: Sparse Probing and Outcome Calibration for On-Policy Distillation
On-policy distillation (OPD) provides dense teacher supervision on student-generated trajectories, but standard reverse-KL training can assign insufficient probability to other plausible continuations. Teacher entropy alone does…