Memory & Context

CodeNib addresses a common bottleneck for coding agents: repeatedly rediscovering the same repository context. It builds lexical, dense, and structural views per commit and serves search, navigation, and bounded context through one runtime.

The paper introduces Agentic Context Management, arguing that production agent failures often stem from poor control over what sits in the reasoning context, not from weak reasoning alone.

Long context is useful only when the right information survives. SWE-Pruner Pro argues that a coding agent already has enough internal signal to decide what tool output should stay or go.

ReflectWorld-MM is designed to help AI systems remember open-ended video streams the way humans do: by tracking persistent people, objects, and changes over time. Its main shift is from frame-based storage to entity-oriented memory.

RecGPT-V3 presents an industrial LLM-based recommender that maintains evolving user memory, grounds intent with Semantic IDs, and compresses reasoning into latent tokens. In Taobao’s “Guess What You Like” feed, the report cites higher engagement and a 52.4% reduction in serving resource consumption.

xHC revisits Hyper-Connections as a practical scaling axis for large language model pre-training. By expanding residual streams to N=16 while sparsely updating only part of them, it reports stronger downstream performance with modest added training cost.

A new paper proposes storing verified knowledge as byte-exact KV-cache artifacts and grafting them back into a frozen small language model. The result is striking efficiency on repeated cases, but the claims also raise questions about reproducibility and what counts as improved reasoning.

A new arXiv paper argues that attention-based KV cache eviction can over-preserve structural tokens in schema-heavy inputs such as nested JSON. The proposed retraining-free role-aware allocation method aims to keep more answer-bearing values under tight memory budgets.

A new arXiv paper introduces HealthClaw, an open-source agent architecture designed for ongoing personal health support. Instead of treating each query in isolation, it updates private memory as routines, preferences, measurements, and risks change.