AI Coding Live News

On November 5 Perplexity Developers (@perplexitydevs) said every Agent API effort level now costs less per query with equal or better research quality, and added an ultra tier for broad long-running research. Vendor figures: medium rose from ~55% to ~77% on BrowseComp/DSQA with nearly 90% lower cost; xhigh held ~76.5% WANDR at ~25% of August 2026 cost; ultra runs three parallel researchers plus a consolidator at ~85.9% WANDR and about $21.71/task.

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On Nov 2 Meituan's LongCat team listed LongCat-2.5 in its API platform change log as the full release of September 25's LongCat-2.5-Preview. The official technical blog says it has 1.6 trillion total parameters with about 48 billion activated per token, builds on LongCat-2.0 with stronger native visual understanding and agentic coding, and was trained and served with W4A4 inference on domestic AI accelerators. Vendor-reported scores include 88.4 on Terminal Bench v2.1, 38.5 on Terminal Bench v4.0, 91.9 on BrowseComp and 83.1 on MCP Atlas. The API speaks both OpenAI and Anthropic formats with a 1M-token context and up to 128K output tokens, at a limited-time price of $0.30 per 1M uncached input tokens, $0.006 cached input and $1.20 output.

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Independent AI evaluation platform Artificial Analysis (@ArtificialAnlys) officially benchmarked DeepSeek's web search API (DeepSeek Search). Augmenting the base model with DeepSeek Search boosted its Search Index score from 33 to 69—an impressive +36 point leap (ranking 17th among 27 tested search APIs). DeepSeek Search established a market-leading price-performance ratio, priced at just $0.75 per 1,000 searches (~$0.032 per completed task), making it the most cost-effective paid search API evaluated. It recorded 70% accuracy on AA-Omniscience and 63% on BrowseComp, highlighting an optimal balance between factual grounding and operational economics.

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Researchers from Stanford University and UC Berkeley introduced Librarian Agents (arXiv:2610.22273). To overcome the bottleneck of reactive RAG methods struggling with complex cross-document reasoning at query time, the framework uses reinforcement learning to train LLMs to perform anticipatory directed curation on messy long-context workspaces into structured synthetic filesystems. On BrowseComp+, it hits 55.86% accuracy while reducing navigation token consumption by 33.8% to 51%.

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Deep search agents struggle with credit assignment across long, noisy retrieved contexts. Zhejiang University and Tencent open-sourced KnowGPO (arXiv:2610.22331, GitHub: ZJU-REAL/KnowGPO), an RL framework that groups search trajectories by atomic knowledge conditions rather than surface observations. Qwen3-8B fine-tuned with KnowGPO achieves 38.8% on BrowseComp-ZH and 40.2% on GAIA, outperforming GRPO/GiGPO baselines and rivaling 32B open-source search systems.

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Standard agentic reinforcement learning assumes single-session trajectories with continuously accumulated context, clashing with production agent techniques like context compaction and subagent forking. Researchers introduce Mosaic (arXiv:2610.22174), a critic-free hierarchical optimization framework for Multi-Session Agentic RL. By incorporating session-usage signals at the trajectory level and shaping per-session advantages by marginal contribution, Mosaic outperforms trajectory-level broadcasting on BrowseComp across both compaction and subagent scenarios while using fewer sessions.

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Test-time self-improvement—the ability of an agent to iteratively refine solutions during inference—relies on high-fidelity reflection to surpass current proposals and long-horizon execution to maintain coherent multi-round exploration. Researchers from Renmin University of China, Microsoft Research, and USTC introduce AREX-2, establishing that these capabilities are domain-agnostic and learnable from verifiable environments. Synthesizing long-horizon reflective trajectories across machine learning and algorithmic programming, an AREX-2 agent built on Qwen3.8-27B achieves 81.8 on MLE-bench Lite and 70.7 on Frontier-CS, transferring seamlessly to deep research with 84.0 on BrowseComp, 92.2 on GAIA, and 93.8 on DeepSearchQA while monotonically scaling with compute budget.

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Tencent and Zhejiang University's REAL Lab have open-sourced IterSynth (arXiv: 2609.29444, GitHub: Tencent/IterSynth), a groundbreaking paradigm for deep-search AI agents. Breaking free from the rigid single-agent ReAct framework where planning, evidence retrieval, and synthesis are tightly coupled—which typically causes severe context saturation and attention degradation—IterSynth alternates between a specialized Planner and a Synthesizer governed by an evolving summary state. Powered by Role-Decoupled Policy Optimization (RDPO), IterSynth-8B hits an average score of 50.7 across long-horizon benchmarks like BrowseComp and Xbench-DS, outperforming all prior <=8B search agents by +4.2%.

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