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Every published item, in order · 7 items

Nov 7

Papers BuildersMulti-sourceSelected08:57

Harness Leaderboard ranks nine coding-agent harnesses on open models

The harness you wrap around an open model can swing both solve rates and cost by large margins — opencode ran to US$3.35 per solved task, 11.7 times Pi's cost, on identical models. Teams self-hosting GLM, DeepSeek or Kimi models should pick a harness per model rather than a single default. The companion Harness Lens documents prompts, tools and compaction across 34 harnesses with source links.

#mini-swe-agent #GLM-5.3-Flash #DeepSeek-V4.1-Flash #coding assistants

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Thomas Wolf@Thom_Wolf6 sourcesSource

Nov 2

Papers Builders13:30

Chinese contentLatent Sparse Attention：让超长上下文注意力计算提速 72 倍

超长上下文跑不动？这篇论文用分层聚类做稀疏注意力，10M token 下比全注意力快 72 倍还不掉点，做长上下文的值得看看。

#Latent Sparse Attention #GLM-5.3-FLASH #稀疏注意力 #长上下文

arXiv cs.AI@Ales Kubicek et al. (10)Source

Oct 27

Papers Builders12:38

Chinese contentREVA：用单次前向传播实现实时漏洞检测

Agent 查漏洞太慢，这篇论文直接读 LLM 内部表示，1 秒判定一个函数，比 agentic 方案快 250 倍还不掉准确率，做安全工具的可以看看。

#REVA #GLM-5.3-Flash #PrimeVul #代码安全

arXiv cs.AI@Amine Lbath, Massih-Reza AminiSource

Oct 23

Papers Builders10:53

Chinese contentS²-MAS 发布：5 亿家庭智能体的大规模社会模拟框架

有人用 5 亿个模拟家庭做经济实验，小模型成绩还压过 GPT-6-Luna，做经济或政策模拟的可以看看这篇。

#S²-MAS #多智能体 #模拟 #经济仿真

arXiv: DeepSeek@Yang Li et al. (21)Source

Oct 15

Papers BuildersMulti-source13:00

Chinese content研究拆解 LLM 网络威胁情报实体抽取失败模式：主要问题不是幻觉

DeepSeek-V4-Flash 和 GPT-6.1-Sol 抽实体都不靠谱，但坏的原因不是幻觉，而是把背景信息当成攻击实体。用置信度分层排审核优先级，别当真值。

#DeepSeek-V4-Flash #GLM-5.3-Flash #GPT-6.1-Sol #网络威胁情报

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arXiv: DeepSeek@Jiahong Qian5 sourcesSource

Oct 14

Papers BuildersMulti-sourceSelected03:50

LongHarness lifts GLM-5.3-Flash on long-context QA

Benchmark scores often reflect the harness as much as the model, and this 4.4% to 61.1% jump on identical weights is a stark demonstration of that gap. Teams evaluating long-context agents should benchmark models with purpose-built harnesses rather than off-the-shelf chat loops, or risk underestimating a model's real capability. The paper (arXiv 2610.13075) offers a concrete design: explicit state for evidence and conclusions that revise each other.

#GLM-5.3-Flash #LongHarness #Zhipu (Z.ai)#agents

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