HarnessDev: Can LLMs Create and Evolve Their Own Agent Harness?
The study introduces a benchmark evaluating whether LLMs can construct and iteratively refine their own agent execution harnesses rather than…
The study introduces a benchmark evaluating whether LLMs can construct and iteratively refine their own agent execution harnesses rather than…
The paper presents DisCo, a research agent that distills operational knowledge from GitHub repositories into reusable AI skills using two…
Researchers introduce WHALE, a method that jointly optimizes both a language model’s weights and its harness code, the executable logic…
The authors present CRISP, a method for speeding up the attention prefilling phase of long-context LLM inference. It replaces a…
The paper tackles a known failure mode in knowledge distillation where student models gain single-attempt accuracy but lose the output…
Researchers developed Debias-SparseGPT, a post-training pruning method that adds a representational-debiasing term computed over demographically contrasting inputs to the standard…
The paper introduces ZipTok3D, a 3D tokenizer designed to reconstruct high-fidelity shapes from extremely short token sequences using progressively informative…
This technical report describes VibeVoice-ASR-Streaming, an LLM-based end-to-end system for real-time, speaker-attributed speech recognition that processes fixed-size audio chunks plus…
The authors present CoGR, a retrieval framework in which large language models generate keyword representations for both queries and items,…
The paper identifies a vulnerability in LLM agents where inaccurate persistent memory can manufacture false permissions, letting an agent take…
Researchers present Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125 billion total parameters that activates only 6 billion per token, achieving…
Researchers introduce Verification-Aware Training (VAT), a framework that improves speculative decoding for large language models. VAT adds a lightweight verification…