Releases·Model Release
Tencent releases ContextPilot models for proactive agent context management
Tencent released ContextPilot, a proactive context-management framework for long-horizon language-model agents, as open-weight checkpoints in 14B, 8B, and E4B sizes. The 14B model is a Qwen3-14B checkpoint trained to plan, maintain long-term memory, and offload less useful context while agents keep reasoning and using tools. Weights are on Hugging Face with an accompanying paper and code repository, giving practitioners a drop-in approach to context management in agentic systems.
6 engagement1 sourceMon, Aug 31, 2026, 08:56 AM
AI Overview
- ContextPilot trains agents to plan, maintain long-term memory, and offload less useful context while continuing to reason and use tools
- Ships as checkpoints in 14B, 8B, and E4B sizes; the 14B model is a Qwen3-14B checkpoint
- Weights published on Hugging Face (tencent/ContextPilot-14B), with code on GitHub (Tencent/ContextPilot) and a paper on arXiv (2608.28476)
Entities
Related
Products·launched
ContextOS: Token-budget-aware context orchestration for LLM agents
ContextOS is an open-source context orchestration layer for long-horizon LLM agents. It uses hybrid retrieval, reranking, and token-budget-aware planning to decide which memories enter the model's context, with execution traces for inspection. It solves the problem of context management for developers building complex LLM agents.
ContextOS is an open-source context orchestration layer for long-horizon LLM agents. It uses hybrid retrieval, reranking, and token-budget-aware planning to decide which memories enter the model's context, with execution traces for inspection. It solves the problem of context management for developers building complex LLM agents.
2 engagement1 sourceredditSun, Aug 23, 2026, 07:15 AM
Releases·Model Release
BAAI releases AREX-2, a 27B long-horizon agent model built on Qwen3.8 with test-time self-improvement
The Beijing Academy of Artificial Intelligence (BAAI) released AREX-2, a 27B-parameter agent model on Hugging Face. It improves solutions across multiple test-time rounds via a propose, measure, reflect, revise loop, trained on ML and algorithmic-programming tasks with verifiable feedback. The learned self-improvement behavior transfers to deep research without new search trajectories.
The Beijing Academy of Artificial Intelligence (BAAI) released AREX-2, a 27B-parameter agent model on Hugging Face. It improves solutions across multiple test-time rounds via a propose, measure, reflect, revise loop, trained on ML and algorithmic-programming tasks with verifiable feedback. The learned self-improvement behavior transfers to deep research without new search trajectories.
100 engagement1 sourceredditWed, Sep 30, 2026, 08:21 AM
Developments·Paper
PRO-LONG paper proposes programmatic memory for LLM long-horizon reasoning
A new arXiv paper introduces PRO-LONG, a programmatic memory approach that enables LLM agents to handle long-horizon tasks by deciding what to save from the environment and how to load it into context. The method targets performance on continual learning benchmarks like ARC-AGI-3, where out-of-the-box models struggle.
A new arXiv paper introduces PRO-LONG, a programmatic memory approach that enables LLM agents to handle long-horizon tasks by deciding what to save from the environment and how to load it into context. The method targets performance on continual learning benchmarks like ARC-AGI-3, where out-of-the-box models struggle.
0 engagement1 sourcearxivWed, Jul 22, 2026, 12:11 PM
Releases·Tool Release
Tencent open-sources Agent Memory engine for long-running AI agents
Tencent has open-sourced TencentDB Agent Memory, a memory engine that helps long-running AI agents manage state by keeping important information close and moving heavy history elsewhere, rather than relying on larger context windows. The approach aims to improve agent performance and efficiency in production.
Tencent has open-sourced TencentDB Agent Memory, a memory engine that helps long-running AI agents manage state by keeping important information close and moving heavy history elsewhere, rather than relying on larger context windows. The approach aims to improve agent performance and efficiency in production.
0 engagement1 sourcerssSat, Aug 1, 2026, 06:16 AM
Developments·Paper
Shared Selective Persistent Memory Architecture Proposed for Agentic LLMs
A new arxiv paper introduces shared selective persistent memory, an architecture that identifies and retains four categories of reusable context (task specifications, data schemas, tool configurations, and tool-use patterns) for agentic LLM systems. This approach aims to solve the context problem where each session starts from zero, while avoiding token-inefficient naive persistence of entire conversation histories. The work is relevant to developers building multi-turn code generation agents.
A new arxiv paper introduces shared selective persistent memory, an architecture that identifies and retains four categories of reusable context (task specifications, data schemas, tool configurations, and tool-use patterns) for agentic LLM systems. This approach aims to solve the context problem where each session starts from zero, while avoiding token-inefficient naive persistence of entire conversation histories. The work is relevant to developers building multi-turn code generation agents.
251 engagement2 sourcesarxiv, youtubeFri, Jul 10, 2026, 03:07 PM