本周AI论文精选：Fugu、Agent记忆、合成数据等

1. Sakana FuguFrontier LLMs keep advancing, and different providers are increasingly specializing in distinct domains, which raises a natural next objective: how do you combine those individual specializations into one collectively intelligent system? Sakana Fugu answers with a family of orchestrator models that are themselves language models trained to read a user query and dynamically devise the agentic scaffold needed to solve it.Orchestrator models, not a fixed pipeline: Fugu is trained to understand a query and build an adaptive agentic scaffold on the fly, harnessing and amplifying a team of LLM agents rather than routing to a single frozen workflow.Performance beyond any single agent: Through these query-adaptive scaffolds, Fugu reaches state-of-the-art results against other publicly accessible models across SWE-Bench Pro, Terminal Bench, LiveCodeBench, GPQA-Diamond, Humanity’s Last Exam, and CharXiv Reasoning.Two models for two regimes: They release Fugu, which balances answer quality against latency for everyday use, and Fugu-Ultra, which prioritizes quality on the hardest problems.Why it matters: The training paradigm combines large-scale fine-tuning, evolutionary algorithms, and reinforcement learning, plus the infrastructure to turn that into a production system, pointing to dynamic, query-adaptive scaffolds and collective intelligence as a path toward the next frontier of AI capabilities.Paper | Tweet2. Agent-Native MemoryMemory for LLM agents has quietly grown from a retrieval add-on into a full data system, with persistent storage, retrieval, update, consolidation, and lifecycle governance running throughout an agent’s execution. Yet most evaluations still score memory only through end-to-end task metrics like F1 and BLEU, treating the whole stack as a black box. This paper studies agent memory from a data management perspective and asks what we are actually missing when we measure it that way.A data management view of memory: The authors argue that operational cost, architectural trade-offs across memory modules, and robustness under dynamic knowledge updates are first-class concerns that task-success metrics hide entirely.A four-module decomposition: They break memory into representation and storage, extraction, retrieval and routing, and maintenance, then evaluate 12 representative memory systems plus two baselines across five workloads spanning 11 datasets.No single architecture wins: Effectiveness depends on how well the memory structure matches the workload bottleneck, and fine-grained ablations quantify each module’s effect on representation fidelity, retrieval precision, update correctness, and long-horizon stability.Why it matters: The study shows localized maintenance is more cost-efficient than global reorganization, and reframing memory as a system with measurable trade-offs is what gets us toward genuinely agent-native memory rather than another leaderboard number.Paper | Tweet3. AutodataBuilding synthetic training data has mostly stayed a fixed pipeline that you hand-tune once and then freeze. Autodata rethinks that by casting an AI agent as a data scientist that builds high-quality training and evaluation data, then meta-optimizes that agent so it learns to create even stronger data over time.An agent as data scientist: Autodata is a general formulation in which an AI agent plays the role of a data scientist building both training and evaluation data, instantiated as a concrete, practical implementation the authors call Agentic Self-Instruct.Meta-optimization compounds the gains: Beyond using the agent to generate data, they train (meta-optimize) the data scientist agent itself, and this self-improvement step delivers a larger performance uplift than base agentic data creation alone.Consistent across domains: On computer science research tasks, legal reasoning, and reasoning with mathematical objects, Autodata beats classical synthetic dataset creation methods, showing the approach is not tied to a single problem type.Why it matters: Agentic data creation turns increased inference compute into higher-quality training data, offering a path that could change how teams build datasets rather than freezing a pipeline and hoping it generalizes.Paper | Tweet4. Critique of the Agent ModelThe word agent now covers everything from a for-loop with tool calls to speculative machine superintelligence, which makes it nearly useless as a technical term. This position paper from Eric Xing and collaborators tries to fix that by asking what an agent actually is and what agency consists of, drawing on Descartes and on science-fiction portrayals of autonomous beings to ground the discussion.Five dimensions of agency: The authors analyze agent architectures along goal, identity, decision-making, self-regulation, and learning, and argue that genuine agency requires these structures to be internalized in the system rather than assembled through external scaffolding.Agentic versus agentive: They draw a sharp line between agentic systems, whose competence lives in engineered workflows, and agentive systems, whose capabilities including social interaction arise endogenously, marking the boundary between task-specific tools and open-world autonomy.A concrete architecture: Building on the analysis, they propose the Goal-Identity-Configurator, combining hierarchical goal decomposition, identity evolution, simulative reasoning grounded in a separately trained world model, learned self-regulation, and self-directed learning from real and simulated experience.Why it matters: Clear definitions are not academic hair-splitting here. They shape what we build and what we should reasonably fear, and the paper centers auditability, controllability, and safety for systems that hold more autonomy yet stay under human oversight.Paper | TweetMessage from the EditorWe just released LLM-as-a-Judge, a hands-on DAIR Academy lab where you build an LLM judge from scratch to evaluate open-ended AI output. Across six short labs, you grade a support bot’s freeform replies on a rubric, then validate the judge against human labels and harden it against bias, ending with a small, trustworthy evaluation harness you can point at any open-ended task.Start LLM-as-a-Judge5. Agent-as-a-RouterMost users now have access to many LLMs that each excel in different domains, so routing each task to the right model matters for both quality and cost. Existing routers treat this as a static, one-off classification problem, and this paper shows that framing is exactly what holds them back.Information deficit is the bottleneck: Simply augmenting a vanilla LLM router with performance statistics at the task-dimension level yields a 15.3% relative gain, surpassing a heuristic router built on the same priors, which pinpoints missing information rather than model choice as the real limiter.Routing as a closed loop: Agent-as-a-Router formalizes routing as a Context, Action, Feedback, Context loop that accumulates execution-grounded experience during deployment instead of deciding once and moving on.A concrete system and benchmark: The framework is instantiated as ACRouter, built from an Orchestrator, a Verifier, and a Memory module, and the authors release CodeRouterBench, roughly 10K task instances scored across 8 frontier LLMs for regret-based comparison on streaming tasks.Why it matters: ACRouter achieves the lowest cumulative regret on in-distribution tasks and generalizes to out-of-distribution agentic programming, showing that treating routing as an experience-gathering agent, not a classifier, is what closes the information gap.Paper | Tweet6. Agent Communication ProtocolsAs multi-agent systems try to move past the limits of standalone agents, communication becomes the load-bearing infrastructure, and the protocol landscape for it is a fragmented mess. This study builds a technical taxonomy to classify and compare LLM agent communication protocols and to make the interoperability problem l

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