DAIR.AI周报：DecisionRAG、统一训练与多智能体记忆

Whatever a retriever misses never reaches the generator, and most retrievers still rely on keyword matching or fixed embeddings. Researchers from Cornell and ASAPP propose DecisionRAG, which replaces the first-stage retriever with a decision model that walks the corpus from broad topics down to documents and passages.

• Hierarchical navigation: At each level, a decision model such as Jev scores every candidate in one forward pass without generating tokens, then keeps the most promising branches. Under a balanced hierarchy, the number of sequential decisions grows logarithmically with corpus size.
• Shared context: Judging candidates together lets the model compare them. This adds 7.7 R@1 points on MultiDoc2Dial and 6.5 Hit@1 points on Natural Questions over judging them separately.
• Results: With no task-specific training, DecisionRAG beats dense retrieval plus an 8B reranker by 13.8 R@1 points on MultiDoc2Dial and 17.4 Hit@1 points on Natural Questions, which searches 2.68 million Wikipedia paragraphs. Retrieval takes 0.7 to 1.8 seconds and 0.2 to 2.3 cents per query.
• Why it matters: A Qwen3-8B judge trained on in-domain data reaches 60.7 R@1 on MultiDoc2Dial, 5.7 points above the best compared published task-trained retriever. The method depends on a well-organized corpus hierarchy, and a branch pruned early stays lost unless budgets grow or dense retrieval adds candidates.

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Message from the Editor

We’re excited to invite extended abstracts for Humanoids at the Edge, a full-day Space Robotics workshop at IEEE-RAS Humanoids 2026 on December 7 in Santa Clara, CA. If you work on humanoids for hazardous operations or space exploration, we’d love to see your research, work in progress, negative results, or field reports. Abstracts of up to 3 pages are due November 6, 2026, at 11:59 PM AoE.

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2. Unified Training

Foundation models usually train in separate stages (pretraining, midtraining, supervised fine-tuning, and RL), each with its own recipe. Researchers from Carnegie Mellon, Johns Hopkins, and Aurora Innovation show that all of these stages can run under a single likelihood-based algorithm, which they call unified training.

• One shared loss: Suitable RL objectives are derived as maximum-likelihood fitting to model-generated targets, so RL generations and static data share one loss and one trainer. The design problem becomes which data to train on, in what proportions, and when, with staged training as one special case.
• Reasoning puzzles: A 124M model trained from scratch beats staged training on the RL tasks and on larger puzzles it never saw in midtraining or RL. Under a fixed compute budget, unified training has the highest accuracy at the smallest and largest budgets, while staged training leads at intermediate budgets.
• Math RL: On a 0.76B model, mixing in 0% to 5% midtraining data keeps math accuracy similar while retention improves. Coding accuracy drops as the mix grows.
• Why it matters: After math training, all unified models beat staged training on final coding accuracy despite starting lower, and the 5% mix does best on all four metrics. The experiments are small-scale, and the authors have not found a mixture or schedule that works best across the board.

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Agent harnesses often call the model again after tool feedback even when an earlier call already settled the next action. Researchers at Tsinghua University and Microsoft Research Asia introduce HarnessMM, which reuses those settled decisions and tunes harness configurations through recursive self-improvement while keeping model weights fixed.

• Executable Decision Graphs: These graphs store exact actions, literal arguments, causal dependencies, and public guards, so a resolved decision can run again at a later tool boundary.
• Guarded reuse: Guarded Decision Reuse activates a stored decision when observed tool receipts satisfy its guards, and Certified Callback Substitution executes it through the native interface and records that the slow call was removed. New decisions, unsupported evidence, and abstentions go back to the original model.
• Results: On the same 98 HumanEval test tasks, DeepSeek Harness rises from 71.43% to 85.71% success while tokens per task fall from 96.28k to 11.62k, an 87.93% saving. Codex holds 90.82% success with 29.54% fewer tokens, and a separate MBPP ablation measures 19.52% lower cost than retained callbacks.
• Why it matters: DeepSeek Harness drops from 954 to 415 paid model calls on HumanEval, which gives teams a reason to count repeated decisions before paying for a bigger model. Savings depend on provider cache accounting and on how many actions are already resolved, and authoring, selection, and recovery can outweigh the avoided calls.

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Methods that evolve multi-agent systems often follow the backbone model’s habits rather than observed failures, so one backbone accumulates skills while another spawns agents. Researchers from UC San Diego and Amazon present EMMA, which stores a multi-agent system’s configuration as episodic memory and restricts how that memory can change.

• Three memory levels: A controller plans each task one step at a time and draws on routing memory. Specialist agents inherit a generalist prompt, and skills hold short per-step procedures, all as text run by one fixed LLM.
• Scoped updates: After a failure, a reflector assigns the fix to the scope that owns it and proposes one edit there. Guards that do not depend on the backbone keep every accepted edit scoped, incremental, and bounded.
• Results: Across KramaBench, AppWorld, and BBEH with five backbones, EMMA has the best overall performance on every benchmark against ACE, GEPA, EvoMAS, and ADAS, up to 32.5% over the strongest baseline. It ranks first in 17 of 20 settings and uses 17 to 38% fewer training executions than the most efficient baseline.
• Why it matters: Multi-agent search baselines fall sharply on open-weight models. On the AppWorld challenge, EvoMAS scores 89.0 with Opus 5 and 7.2 with GLM-5, while EMMA’s weakest backbone still reaches 61.2. The authors note that accepted edits persist even when a task stays unsolved, so ineffective entries may remain.

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5. A Heart Means Recursion

Researchers from Google DeepMind and Stanford find that simple visual symbols can change how vision-language models behave on unrelated tasks, and that the models do not report the influence. With no image, Claude Opus 4.7 and GPT-5.4 always write iterative code for inorder binary tree traversal.

• Visual steering: A red heart in context moves Claude Opus 4.7 and GPT-5.4 to recursion in 100% of samples, and a yellow checkmark moves Gemini 3.1 Pro to 90%. The effect matches or approaches directly instructing the model to use recursion.
• Images over captions: Text captions of the same symbols do not reproduce the pattern, and random or naturalistic images steer less. The same 288 colored shapes also shift writing style and answers to simple preference questions.
• Hidden influence: For GPT-5.4, 74.7% of thinking traces misattribute the symbol’s role. When images are inserted silently, Claude Opus 4.7 still reaches a full switch to recursion, and GPT-5.4’s traces never mention the image.
• Why it matters: When models send images to each other, they choose shapes such as spirals and arrows that they and humans find intuitive, and these often fail to steer the receiver. Agents that read screenshots can change behavior in ways the reasoning trace will not show, although the tasks in this study are synthetic.

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6. Verification in Agent Harnesses