AI Agent Papers2026
Measures how coding agents explore repositories by asking them to return a ranked, line-level list of code regions relevant to an issue under a fixed line budget. Covers 848 issues across 203 repos and 10 languages; evaluates coverage, ranking, and context-efficiency to isolate exploration quality.
Removes the subspace of frequent, uninformative tokens that LLMs inject into text embeddings via the model's unembedding matrix. EmbedFilter is a lightweight linear transform that refines LLM-derived embeddings to improve zero‑shot semantic retrieval, enable dimensionality reduction, and speed up indexing; code on GitHub.
AI Agent2026
Provides a virtual, durable filesystem and pluggable execution runtimes for agents running inside Cloudflare Durable Objects. Offers container, isolate-shell, and isolate-JS backends; preview-stage API with ~10GB Durable Object backing and FUSE-mounted container trade-offs.
AI Agent2026
Provides reusable AI agent skills that turn public social posts, transcripts, comments and ad libraries into actionable research artifacts. Workflow-first design covers outlier-post detection, comment mining, transcript intelligence, competitor teardowns and content repurposing for agent-driven automation.
Standardizes representation-level evaluation for tabular encoders by exporting row-, column-, and table-level embeddings and probing them with shared lightweight heads across three suites (TRL-CTbench, TRL-Rbench, TRL-DLTE). Supplies curated benchmark assets and task rewrites (50 OpenML tables, 123 targets, a 47,772-table DLTE lake) to enable fair cross-paradigm comparison.
Generates outcome-specific, dialectical rationales with an LLM and derives continuous, calibrated risk scores for irregularly sampled medical time series—mitigating risk polarization. Reports +3.3% average AUPRC and 81% reduction in calibration error across three benchmarks; code released.
AI Agent Papers2026
Turns raw datasets into verifiable multimodal news features via a multi-agent newsroom pipeline. Key innovations: (1) an Inspector that links each claim to data/code/external references for re-execution and audit; (2) multimodal asset generation (interactive maps, audio, visuals) tailored to the story.
AI Agent Papers2026
Synthesizes shortcut-resistant search tasks to train deep search agents by controlling four shortcut risks across entity selection, evidence-graph construction, question formulation, and adversarial refinement. Produces training trajectories with longer pre-answer search and fewer shortcut patterns; code will be released on GitHub.
Implements a blockwise sparse attention (MiniMax Sparse Attention) that scores and Top-k selects key-value blocks per Grouped Query Attention group to enable attention over million-token contexts. Paired with an exp-free Top-k GPU kernel and KV-outer sparse execution, it reduces per-token attention compute and yields large prefill/decoding speedups.
AI Agent2026
Routes natural-language requests to a single “first mate” agent that spawns and supervises multiple autonomous crewmates, each running in an isolated git worktree and producing finished PRs, approved local merges, or standalone investigation reports. Key features include visible session backends, disposable worktrees, explicit project modes, optional persistent secondmates, and an event-driven zero-token watcher.
AI Agent2026
Provides a ruleset and skills that make AI coding agents prefer the simplest correct implementation: reuse existing code, prefer stdlib/native features, and only write minimal new code. Cuts generated LOC, tokens, cost and time while preserving validation and safety.
Learns, maintains, and runs unified world models for Physical AI using a cross-embodiment pretraining curriculum and a hybrid linear temporal-attention architecture. Emphasizes long-horizon state persistence, theoretical bounds on error accumulation, and deployment-aware low-latency inference for real-world embodied agents.