@NielsRogge: Top trending paper on http://paperswithco.de is "FastContext: Training Efficient Repository Explorer for Coding Agents"…
Summary
Microsoft's FastContext is a trending paper introducing a small 4B model for efficient code retrieval paired with coding agents, rivaling closed-source systems on SWE-Bench Multilingual.
View Cached Full Text
Cached at: 06/16/26, 05:39 PM
Top trending paper on http://paperswithco.de is “FastContext: Training Efficient Repository Explorer for Coding Agents”
Microsoft trained a small 4B model, available on @huggingface, that can be paired with a coding agent for efficient code retrieval.
This way, they are able to rival the closed-source ones on SWE-Bench Multilingual
Read more here: https://paperswithcode.co/paper/2606.14066…
Similar Articles
FastContext: Training Efficient Repository Explorer for Coding Agents
FastContext introduces specialized exploration models that separate repository exploration from code solving in LLM agents, reducing token consumption by up to 60% while improving resolution rates on software engineering benchmarks.
SWE Context Bench just proved something I think a lot of coding agent users already feel
A new benchmark paper 'SWE Context Bench' tests whether coding agents can reuse knowledge across tasks, highlighting a gap in existing benchmarks that only evaluate isolated problem-solving. The author discusses solutions like external memory and mentions tools such as langmem, mem0, supermemory, and Greplica.
I built a context window optimization framework for coding agents — open source + paper
The author introduces 'Apohara Context Forge,' an open-source framework and methodology for optimizing context windows in coding agents using role-aware segmentation and tiered relevance scoring.
@rohanpaul_ai: Meta paper shows that coding agents get much better when they reuse short summaries of past attempts instead of raw log…
A Meta paper shows that coding agents improve significantly when they reuse short summaries of past attempts instead of raw logs, achieving strong gains on SWE-Bench and Terminal-Bench with Claude 4.5 Opus.
@NielsRogge: Impressive release by StepFun, explore it at https://paperswithcode.co/paper/83892
StepFun releases Step 3.7 Flash, an open-weight model designed for agentic, coding, search, and multimodal tasks, achieving top scores on several benchmarks.