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Recurrent memory for long-context agents
A paper published on September 29, 2026 identifies retention as a bottleneck in recurrent-memory agents and presents a training-free approach that selects one memory head and preserves the others.
Byte and token models: scaling results up to 1 trillion bytes
A paper published on September 14, 2026 compares distilled byte and token models and reports a higher performance ceiling for byte models as compute increases.
- 19 days ago huggingface.co
NatureBench evaluates coding agents on research
Published on June 24, 2026, the post describes a benchmark with 90 tasks based on articles from the Nature family. The best agent exceeded the published reference result on 17.8% of tasks.
- 19 days ago github.com
Code notebooks explore agent design patterns
Published on August 25, 2026, Antonio Gulli’s guide presents notebooks for exploring patterns including prompt chaining, routing, reflection, tool use, planning, and multi-agent systems.
NOVA applies verified agents to recommendation systems
A description published on June 26, 2026 presents an agent harness for evolving recommendation architectures under domain, interface, resource, and serving constraints.
- 19 days ago github.com
Octop combines multi-user agents with self-hosting
A Tencent Cloud post published on September 15, 2026, presents Octop, an open-source, self-hosted AI assistant platform with local data storage and team integrations.
- 19 days ago github.com
OpenShell proposes a secure runtime for autonomous agents
Published on September 28, 2026, the GitHub repository describes OpenShell as a runtime focused on the security and privacy of autonomous AI agents.
Ornith-1.0: open models for coding agents
Announced on June 25, 2026, the Ornith-1.0 family spans models from 9B to 397B parameters, trained with reinforcement learning to optimize code and the scaffolds that guide agents.
Agent orchestration can reduce costs and latency
Published on July 13, 2026, the paper reports lower costs and latency with equivalent quality across 22 tasks and six models after changing orchestration.
PACE predicts agent performance with few tasks
A July 6, 2026 post presents a method to estimate agent results from a small task set at lower cost than a full evaluation.
Pandora’s Router balances estimation and routing costs
A paper published on August 22, 2026 proposes using cheap, noisy estimates before paying for better evaluations. On MATH, RAG, and EmbedLLM, it achieved or tied for the lowest average combined inspection cost and routing regret.
ReContext: improving evidence use in long contexts
Published on July 6, 2026, the ReContext paper reports improved evidence use across eight 128K datasets, with three model backbones and no training.
Red Queen Gödel Machine coevolves agents and evaluators
A paper published June 29, 2026 describes a method that evolves agents and evaluators, keeping evaluators fixed per epoch and replacing them only after better performance on reserved ground truth.
Rejection sampling for on-policy LLM alignment
A paper published on July 7, 2026 proposes using rejection sampling to treat accepted off-policy tokens as on-policy and address the variance of accumulated per-token importance sampling ratios.
Elastic sandboxes for training and evaluating agents
Published on September 23, 2026, the article describes stateful, elastic execution environments for large-scale agent training and evaluation, as well as defenses against agents exploiting training environments.
- 19 days ago github.com
Semantica: knowledge graphs and agent decision traces
On August 8, 2026, the publication about the open-source Semantica project describes self-hosted infrastructure for turning data into knowledge graphs and recording agent decisions.
Agent serving: measure bottlenecks beyond inference
A paper published on August 21, 2026 presents AgentSysBench, covering 10 agentic applications, and reports that inference is not always the main serving bottleneck.
Survey maps self-improvement in AI agents
Published on July 19, 2026, the survey reviews 239 papers on improving agents through model updates or changes to components such as prompts, memory, and tools.
ThetaEvolve: test-time learning
Published on July 11, 2026, the open-source ThetaEvolve framework combines in-context learning and test-time reinforcement learning to explore open-ended optimization problems.
Three autonomy levels for agentic recommendations
A survey published on July 7, 2026 proposes a taxonomy of recommendation systems using LLM-based agents, organized by level of autonomy.
- 19 days ago alphaxiv.org
Verification for long-running agents
A post published on August 8, 2026 says the Argus runtime conditions pivots on verification and reports 78% on SWE-Bench Pro, compared with 59% for Direct Copilot.
VeriHarness verifies agents on long-horizon tasks
A paper published on October 4, 2026 presents a method for checking agent claims against workspace evidence, without reference answers or evaluation rubrics during testing.
WikiSkill: persistent knowledge for agent skills
Published on August 28, 2026, the WikiSkill description separates execution traces, a persistent knowledge wiki, and executable skills to reuse experience across iterations.
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