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Context Engineering Inside the Harness: 4 Mechanisms That Beat Context Overflow and Goal Loss on Long-Horizon Tasks

Survey of four harness mechanisms—context budgeting, compaction, todo-state, and memory—that keep long-horizon LLM agents on task across 200+ tool calls.

The article details how agent harnesses, not larger context windows, solve context overflow and goal loss on long-horizon tasks, citing Chroma's Context Rot report showing 18 LLMs (GPT-4.1, Claude 4, Gemini 2.5, Qwen3) degrade on long inputs. Concrete implementations include LangChain Deep Agents offloading tool responses over 20,000 tokens to the filesystem and truncating old tool calls at 85% window usage, and Claude Code capping auto memory at 25KB while re-reading the 5 most recently modified files after compaction. OpenAI's Responses API now offers server-side compaction via context_management with a standalone /responses/compact endpoint, which Codex uses for long-running coding tasks. Manus reports a roughly 100:1 input-to-output token ratio per ~50-tool-call task, motivating todo.md state recitation to prevent goal drift.

MarkTechPostSep 13, 05:56 UTC · 4d agoAI research2

Flattening Every Memory Peak in Long-Context Mixture-of-Experts Training

Four scheduling techniques bound all memory peaks in long-context MoE training, enabling 120B-667B models at 1M-token context with up to 10.4x throughput.

The paper addresses memory peaks in long-context Mixture-of-Experts training by bounding four unbounded components: expert dispatch with the routing matrix, vocabulary projection, gradient checkpoint boundaries, and optimizer state. It introduces PipelinedLLEP (capped token contributions per dispatch chunk), Ring-DTP (ring circulation of activations or weight shards with online log-sum-exp), Selective Checkpoint Offload (SCO), and OffloadStreamAdamW, all preserving exact loss and gradients. Composed on MoE models from 120B to 667B parameters, the methods enable training at 1M context length, 8-32x the context reach of a tuned FSDP2 baseline, and up to 10.4x its throughput.

Hugging Face daily papersSep 12, 20:00 UTC · 4d agoAI research1