AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
Zero-Mem
Production memory stacks spend extra model calls on summarizing interactions, writing records, and reranking retrievals. Each of those calls costs tokens and latency, and the generated summaries quietly discard the evidence you later need. This work asks whether structured memory access requires generation at all.
Progressive Disclosure, Measured
Agent Skills package expertise into folders an agent loads on demand, and progressive disclosure exposes only what a query needs, from a short description down to specific passages. Practitioners adopted this pattern fast for book-length tasks, but the supporting evidence was anecdotal until now.
ReContext
Models now support 128K context windows yet still fail to use evidence already sitting in the prompt. ReContext is a training-free inference harness for long-context reasoning that uses model-internal relevance signals to build a query-conditioned evidence pool, then replays it right before final generation while preserving the full original context.
BlockSearch
BlockSearch runs the first systematic study of in-context retrieval at the scales real retrievers actually face, million-token corpora and length generalization far beyond training size. It introduces a 0.6B language-model retriever whose architectural and training changes improve over prior LM baselines and length-generalize up to 10 times beyond their training length, pointing toward retrievers that stay reliable as context windows keep growing.
Generative Skill Composition
Coding agents accumulate large skill libraries, and picking the right skills for a task has become the bottleneck. The usual options either dump the whole collection into context or retrieve skills with embeddings and rerankers, and both treat selection as a ranking problem rather than a joint plan. ---
Compositional Skill Routing
Real tasks rarely map to a single skill. They usually need several skills composed together, yet most skill routing still treats the problem as picking one tool from a library. This work formalizes Compositional Skill Routing, where an agent must select and sequence multiple reusable skills from large libraries to satisfy a complex query, and introduces SkillWeaver, a decompose, retrieve, and compose pipeline built around it.
AtomMem
Long-term memory for LLM agents tends to fail in two ways: coarse summaries drift over time, and unconstrained updates corrupt what was already stored. AtomMem keeps the unit of memory small, using a Fact Executor that selectively extracts high-value atomic facts from long interactions and organizes them into hierarchical event structures and temporal user profiles, with an associative memory graph that reconnects fragmented memories at retrieval. The approach reports state-of-the-art results on the LoCoMo long-term memory benchmark.
State-Externalizing Harnesses
Harness-1 is a 20B search agent trained with reinforcement learning inside a stateful harness that offloads routine bookkeeping to the environment. The argument is that search agents are usually trained as policies over a growing transcript, forcing RL to optimize both genuine search decisions and recoverable state like which evidence is useful or which claims are checked. Harness-1 moves that state out of the policy and into an environment-side working memory of candidate pools, an importance-tagged curated set, compact evidence links, and verification records. The 20B agent reaches an average curated recall of 0.730 across eight retrieval benchmarks, beating open-source baselines by 11.4 points and matching or outperforming much larger frontier searchers, with stronger generalization on unseen domains.
The Efficiency Frontier
Context costs dominate production LLM bills, and the right strategy depends on how often preprocessing gets reused. This paper models context-strategy selection as a deployment-aware optimization problem that jointly accounts for task performance, token cost, and reuse, then uses it to compare retrieval-based and preprocessing-based approaches under realistic constraints.
Memory as a Model
MeMo augments any frozen LLM with a separately trained memory model that stores, retrieves, and integrates facts on the base model's behalf. Memory updates are decoupled from base-model weight updates, so the system supports continual learning without catastrophic forgetting, a property RAG fails to deliver because a vector store is just a database with a learned encoder bolted on.
Is Grep All You Need?
The paper evaluates grep-style text search against embedding-based retrieval inside coding agents. When wrapped in a suitable agent harness, grep matches or exceeds embedding retrieval on coding-agent tasks. The study isolates the contribution of the harness from the contribution of the retrieval primitive, and finds that harness design accounts for most of the performance differential typically attributed to embeddings.
From Skill Text to Skill Structure
SKILL.md files entangle invocation interface, execution flow, and tool side effects in a single blob of natural language. That makes downstream discovery and risk review brittle as skill registries scale. This paper proposes SSL, a three-layer typed JSON representation drawn from Schank and Abelson's classical work on scripts, MOPs, and conceptual dependency. An LLM-based normalizer converts existing SKILL.md files into the structure, so adoption does not require rewriting registries by hand.
When to Retrieve During Reasoning
Most RAG systems retrieve once, before the model starts reasoning. Large reasoning models like o1 and R1 do not work that way. They generate 12k to 25k token chains of thought and hit knowledge gaps mid-inference, long after the retrieval window closed. ReaLM-Retrieve is a reasoning-aware retrieval framework that injects evidence during multi-step inference, detects uncertainty at reasoning-step granularity, and learns a policy for when external evidence actually helps. It achieves +10.1% absolute F1 over standard RAG across MuSiQue, HotpotQA, and 2WikiMultiHopQA, with 47% fewer retrieval calls than fixed-interval IRCoT, and hits 71.2% F1 on 2-4 hop MuSiQue with only 1.8 retrieval calls per question.
Skill-RAG
Most RAG systems retrieve on every query, whether the model needs help or not. This is wasteful when the model already knows the answer and often too late when it does not. This paper introduces Skill-RAG, a failure-state-aware retrieval system that uses hidden-state probing to detect when an LLM is approaching a knowledge failure, then routes the query to a specialized retrieval strategy matched to the gap.
MASS-RAG
Most real-world RAG failures come from retrieving technically-relevant but contextually useless documents, then forcing a single model to reconcile them. MASS-RAG is a multi-agent synthesis framework for retrieval-augmented generation where specialized agents handle distinct roles: retrieving candidate documents, assessing their actual relevance to the query, and synthesizing the final answer from evidence that actually contributes. Instead of one model doing everything, responsibility is decomposed across coordinated evaluators, which fits the direction the field is heading for deep research agents.
Agent Skills in the Wild
Agent skills look great in demos. Hand them a curated toolbox, and they shine. But what happens when the agent has to find the right skill from a library of 34,000? This paper from UC Santa Barbara and MIT presents the first comprehensive study of skill utility under progressively realistic settings, revealing that the benefits of skills are far more fragile than current evaluations suggest.
Meta-Harness
Researchers from Stanford and MIT introduce Meta-Harness, an outer-loop system that automatically searches over harness code for LLM applications. The performance of LLM systems depends not only on model weights but also on the harness: the code that determines what information to store, retrieve, and present to the model. Yet harnesses are still designed largely by hand, and existing optimizers are poorly suited to the task.
Coding Agents as Long-Context Processors
This research asks whether long-context processing can be externalized from latent attention into explicit, executable interactions. Instead of scaling context windows, the authors let coding agents organize text in file systems and manipulate it using native tools, evaluating them on tasks spanning long-context reasoning, retrieval-augmented generation, and open-domain question answering with corpora containing up to three trillion tokens.
KARL
Databricks presents KARL, a system for training enterprise search agents via reinforcement learning that achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic search tasks. The work also introduces KARLBench, a new evaluation framework spanning six search domains.
AgentIR
Deep research agents generate explicit reasoning before every search call, but existing retrievers completely ignore these rich signals about search intent and problem context. AgentIR introduces reasoning-aware retrieval that jointly embeds the agent’s reasoning trace alongside its query, along with DR-Synth, a data synthesis method for generating training data from standard QA datasets. On BrowseComp-Plus, AgentIR-4B achieves 68% accuracy with Tongyi-DeepResearch compared to 50% with conventional embedding models twice its size and 37% with BM25.
Diagnosing Agent Memory
This paper introduces a diagnostic framework that separates retrieval failures from utilization failures in LLM agent memory systems. Through a 3x3 factorial study crossing three write strategies with three retrieval methods, the authors find that retrieval is the dominant bottleneck, accounting for 11-46% of errors, while utilization failures remain stable at 4-8% regardless of configuration. Hybrid reranking cuts retrieval failures roughly in half, delivering larger gains than any write strategy optimization.
xMemory
xMemory argues that standard RAG retrieval is a poor fit for agent memory because the evidence source is a bounded, coherent dialogue stream where candidate spans are highly correlated near-duplicates. Fixed top-k similarity retrieval collapses into a single dense region, returning redundant context, while post-hoc pruning can break temporally linked evidence chains. xMemory replaces this with hierarchical memory construction and structure-aware top-down retrieval.
InfMem
InfMem is a cognitive agent for ultra-long document QA that uses System-2-style control to actively manage bounded memory. Instead of passively compressing each chunk as it streams in, InfMem runs a PreThink-Retrieve-Write loop that monitors evidence sufficiency, fetches missing facts from anywhere in the document, and compresses everything into a fixed-size memory - then stops early once it has enough.
A-RAG
A-RAG is an agentic RAG framework that gives LLMs direct access to hierarchical retrieval interfaces instead of relying on fixed retrieval algorithms or predefined workflows. The agent autonomously decides what to search, at which granularity, and when to stop - representing a paradigm shift from static retrieval pipelines to truly agentic information gathering.