Skip to content
Papers.

DarwinX: Evolving Agent Harnesses Through Natural Selection research paper by Salesforce, 2026

Salesforce · Jul 31, 2026 · Agents and evaluation · 116 upvotes 8 weeks ago

Read on arXiv

What it shows

DarwinX evolves agent harnesses via population selection with frozen models, improving verified performance across benchmarks without benchmark-specific patches.

Hugging Face's summary; not yet checked by hand.

More from Salesforce

All 33
PaperCitations
Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM AgentsKeeps an agent's raw past runs and writes a task-specific memory only when a new task arrives, rather than deciding up front what to keep.Agents and evaluation · Sep 20263 days ago-
Flattening Every Memory Peak in Long-Context Mixture-of-Experts TrainingBounds the four memory peaks that crash long-context mixture-of-experts training, from expert dispatch to optimizer state, with fixed-size GPU schedules.Architectures · Sep 202613 days ago-
RISE: Recursive Improvement via Self-Extrapolating Policy DistillationRISE improves language model post-training by recursively generating dense token-level supervision from the model's own reinforcement learning trajectory via self-extrapolation, avoiding external teachers.Training and scaling · Sep 20263 weeks ago-
EVOHARNESSBENCH: Can Your Agents Keep Pace with an Evolving Harness?The study introduces a benchmark to evaluate LLM agents under evolving tool, skill, and agent harnesses, revealing persistent gaps in retention, adaptation, and harness-induced forgetting.Agents and evaluation · Sep 20263 weeks ago-
Random Attention: Rethinking KV Cache Eviction for Efficient ReasoningRandom eviction of reasoning tokens matches selective KV cache compression because reasoning traces are self-protecting through redundancy, making scoring unnecessary once prompts are preserved.Reasoning · Sep 20263 weeks ago-
StateAct: Program State, before Pixels, for Long-Horizon Computer-Use AgentsStateAct improves computer-use agents by grounding actions, verification, and memory in direct program state rather than screenshots, boosting success rates while reducing cost.Agents and evaluation · Jul 20262 months ago-
Evidence-Backed Video Question AnsweringEvidence-Backed Video Question Answering requires models to provide answers with precise spatio-temporal segmentation evidence, revealing gaps between reasoning and visual grounding that are improved by large-scale instruction tuning.Multimodal and robotics · Jul 20262 months ago-
Learning from Language Feedback via Variational Policy DistillationVariational Policy Distillation enables reinforcement learning from language feedback by co-evolving teacher and student policies through variational expectation-maximization, overcoming limitations of passive distillation in complex reasoning tasks.Reasoning · May 20264 months ago-

Other agents and evaluation papers

Topic
PaperCitations
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic CapabilitiesGemini 2.X model family, including Gemini 2.5 Pro and Flash, offers superior coding, reasoning, and multimodal understanding capabilities across a range of computational efficiencies.Google · Jul 2025 · Unverified1 year ago4,134
DeepSeek-V3.2: Pushing the Frontier of Open Large Language ModelsDeepSeek-V3.2 introduces DeepSeek Sparse Attention and a scalable reinforcement learning framework, achieving superior reasoning and performance compared to GPT-5 and Gemini-3.0-Pro in complex reasoning tasks.DeepSeek · Dec 2025 · Unverified9 months ago761
WebWatcher: Breaking New Frontier of Vision-Language Deep Research AgentWebWatcher, a multimodal agent with enhanced visual-language reasoning, outperforms existing agents in complex visual and textual information retrieval tasks using synthetic trajectories and reinforcement learning.Alibaba (Qwen) · Aug 2025 · Unverified1 year ago117
SkillOpt: Executive Strategy for Self-Evolving Agent SkillsSkillOpt introduces a systematic text-space optimizer for agent skills that trains skills as external agent state with stable updates and zero deployment inference overhead, achieving superior performance across multiple benchmarks and execution environments.Microsoft · May 2026 · Unverified4 months ago85
Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic ReasoningWe present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model.NVIDIA · Dec 2025 · Unverified9 months ago81
AgentFold: Long-Horizon Web Agents with Proactive Context ManagementAgentFold, a novel proactive context management paradigm, enhances long-horizon task performance through dynamic context folding, achieving superior results on benchmarks compared to larger models and proprietary agents.Alibaba (Qwen) · Oct 2025 · Unverified11 months ago77
About this paper
Authors
Yifan Zhang, Yutong Dai, Juntao Tan and 9 more
arXiv
2608.07545 · PDF
Citations
Not counted yet · Semantic Scholar
Upvotes
116 · Hugging Face
Code
github.com/SalesforceAIResearch/Beagle
Lab
Salesforce · on Companies · on Acquisitions · on Quarterly · on Paydays · on TechConf

Changes

What changed
New paperAdded to the listSep 26, 2026today

New papers by email

Monday afternoons, only in weeks with new papers from the labs.

Double opt-in. Unsubscribe any time.