token-efficiency

Particle-1, a coding-agent-focused model emphasizing token efficiency (~45% fewer output tokens than DeepSeek V4.1 Flash with comparable accuracy), is now available in OpenClaw v2026.10.10, with zero data retention for standard inference.

The author agrees with poteto, criticizing adversarial code review between sub-agents as a waste of tokens, pointing out its three fatal flaws — meaningless nitpicking, over-engineering, and mismatched compute — and noting that top teams are shifting toward real testing, formal verification, and actual red-teaming to find issues.

DeepSeek showed that an AI agent given 20 tasks in a 300-message group chat delegated none of them until a two-layer Router/Worker design took the keyboard away from the top agent, cutting slowest-task time from ~30 to under 16 minutes while spending 23.2M cached tokens per run.

The HarnessMM paper, a collaboration between Tsinghua University and Microsoft Research Asia, proposes improving agent harness token efficiency through protected decision reuse and recursive self-improvement. Experiments show DeepSeek Harness (DSH) benefits significantly: HumanEval success rate rises from 71.43% to 85.71% with 87.93% fewer tokens per problem, while Codex only saves 29.54%.

Folk personal agent achieved state-of-the-art browser use performance after switching its harness to Browser Use Pi, reporting over 70% fewer tokens, 22% faster execution, and preserved browser state across follow-ups.

Vercel's fx, a tiny open-source coding agent CLI written in Zig, ships v0.0.16 with better prompt caching, 41% fewer uncached input tokens, ~60% smaller npm downloads, and a 2.8% smaller binary.

SwarmChat team benchmarked 1–16 coding agents on ProgramBench, finding that parallel agent teams trade ~36% more tokens for ~5 hours of speedup, but don't consistently beat solo agents given enough time.

Particle-1, a token-efficient coding model post-trained on DeepSeek V4.1 Flash, is now available on the Capy codegen agent swarm harness, offering 1M context and image input at $0.25/$1 per million tokens.

Suhail shares an independent validation showing particle-1 achieving the same performance on a real-world software engineering benchmark with 43% fewer tokens.

Parallel Web Systems reports that Particle-1 ranks #2 in efficiency on the Parallel Search Capability Leaderboard, matching DeepSeek V4.1 Flash's web research performance with 42% fewer output tokens. Suhail shared excitement about the independent validation of their efficiency claims on real-world tasks they couldn't train on.

Linus Ekenstam shares and praises Suhail's announcement of new company Particle and its first developer-focused model, particle-1, which achieves performance parity while using about 45% fewer tokens.

Box CEO Aaron Levie predicts AI agents will consume 1,000X more tokens as they run workflows and build software, while Suhail announces new company Particle and its token-efficient developer model particle-1, which achieves parity performance with ~45% fewer tokens.

This ICAIF '26 paper studies hindsight self-distillation for multi-step financial reasoning on GPT-OSS-20B, finding that a fixed generic feedback line matches the ~3-point gain of self-written critiques, suggesting the positional information in hindsight distillation reduces the need for privileged feedback.

The paper introduces MACR, a relay-based multi-agent framework that dynamically transfers reasoning ownership between agents using confidence-guided triggering and a bidding mechanism, improving accuracy on benchmarks like MathVista (+3.3%) and MMStar (+3.9%) while cutting token overhead by up to 6.0x versus other multi-agent strategies.

The paper introduces Signals and Queries (S&Q), a lightweight first-stage relevance filter for Parlant 4.0 that uses offline-trained logistic classifiers on LLM-generated examples to cut at least 30% of candidate behavioral policies per turn while preserving high recall, outperforming top-K and cosine-threshold retrieval.

该论文提出 LTEM（LLM-Friendly Table-Enhanced Markdown），一种在 Markdown 基础上最小扩展以显式编码合并单元格与多级表头的格式，并构建了表结构复杂度分类体系（F1-F6）和受控基准 STB（1,089 张表）。实验表明 LTEM 在表格理解任务上达到与 HTML 相当的准确率，同时在 token 成本与响应稳定性上优于 HTML/LaTeX。

Ogcode 的团队分享了其用户数据洞察：开发者安装大量 skills 和 MCP 工具导致 agent 选错工具并浪费高达 77% 的 token，他们正在研发能按需动态选择工具的解决方案，并向社区征集思路。

Exa 3.0 launches as the biggest search quality update to Exa's search API for AI agents, featuring a larger embedding model, billions of new documents, Dynamic Highlights that use 95% fewer tokens, and new pricing tiers starting at $4 per 1,000 requests.

研究发现安全漏洞挖掘AI代理60.4%的token用于理解代码与推理触发方式，并提出AVRI工具复用代码关系知识，在Codex和OpenCode上分别降低18.0%和23.7%成本且不损失成功率。

The paper introduces TokenEconomizer, an experience-driven external controller that reduces token costs of coding agents by injecting micro-skills, pruning irrelevant tools, and interrupting failure loops, achieving 18.4–30.9% savings across Claude Code, Codex, and GitHub Copilot CLI with comparable task success.