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Automates distillation of heterogeneous traces from a target person or role into versioned, inspectable skill packages for LLM agents — producing separate capability and bounded-behavior tracks that support natural-language corrections, rollback, and cross-host installation. Ships with an open system and a skills gallery.
Uses search-agent reading traces and tiered distractors to train LLMs for long-context, multi-hop reasoning, and introduces a rubric reward that supervises entity-level steps (applied only to correct finals). Improves evidence-grounded reasoning and resists reward hacking across 4B–30B models.
Proposes TrOPD, a method that restricts token-level on-policy distillation to regions where teacher supervision is reliable to stabilize training under teacher–student distribution mismatch. Adds outlier handling (clipping, masking, forward-KL) and off-policy guidance; shows consistent gains on math reasoning, code generation and general benchmarks.
Studies small trainable adapters (PEFT) used as persistent personal models on top of large foundation models, analyzing three scaling axes—Scale Up, Scale Down, Scale Out—and introducing MinT, an infrastructure for adapter identity, provenance, evaluation, and serving.
A benchmark for evaluating web-browsing agents in Korean contexts, composed of 400 tasks (300 manually verified by native speakers). Includes a human-verified split and an adversarial synthetic split to probe failure modes; reveals large performance gaps for both frontier and Korean models.
Benchmark for evaluating proactive LLM mediators in realistic, multi-domain conflict scenarios by constructing cases from real disputes, probing five socio-cognitive adaptation axes, and using a topic-localized evaluator that achieves 0.82 alignment with human experts.
Benchmark that measures an agent's ability to discriminate fine-grained relational structure in long-term memories. It embeds relation-controlled memory variants into realistic user–agent histories and tests downstream recovery and reasoning, highlighting where current memory systems fail.
Evaluates whether role-playing language agents follow a character's evolving psychological arc rather than a fixed persona, using ArcANE — an automatically constructed benchmark spanning 17 novels and 80 principal characters. Tests both in-text and out-of-text scenarios and compares context strategies and fine-tuned models.
Trains LLMs with reinforcement learning using a surface chrF reward so models learn to extract and apply linguistic signals from rich context for translating completely unseen languages. Demonstrates better zero-shot translation than in-context learning or supervised fine-tuning, framing outcome-based RL as a meta-skill for language learning from context.
Removes the subspace of frequent, uninformative tokens that LLMs inject into text embeddings via the model's unembedding matrix. EmbedFilter is a lightweight linear transform that refines LLM-derived embeddings to improve zero‑shot semantic retrieval, enable dimensionality reduction, and speed up indexing; code on GitHub.
Survey of methods for engineering interactive environments for LLM-based agents, covering environment modeling, symbolic and neural synthesis, evaluation, and agent–environment co-evolution. Identifies evolution paradigms and future directions like Environment-as-a-Service and multi-agent systems.
Proposes a router redesign for Mixture-of-Experts (MoE) that aligns each router row with its expert's principal singular direction using Manifold Power Iteration (MPI), improving token–expert affinity. MPI applies a 'power‑then‑retract' step to push router rows toward principal singular vectors while enforcing norm constraints; the paper gives convergence theory and pretraining results on 1B–11B MoE models.