{"type":"post","id":"proxyattn-a-training-free-method-from-researchers-at-59312c","slug":"proxyattn-a-training-free-method-from-researchers-at-59312c","title":"ProxyAttn , a training-free method from researchers at Harbin Institute of Technology and Baidu, selects those blocks using exact token-level scores from one or more representative attention heads.","description":"ProxyAttn tests whether attention-block selection can be made more precise without training a separate estimator.","retrieval_nugget":"ProxyAttn tests whether attention-block selection can be made more precise without training a separate estimator.","published_at":"2026-10-15","updated_at":"2026-10-15","record_date":"2026-10-15","date_kind":"published_at","topics":["models","inference"],"source_urls":["https://arxiv.org/abs/2509.24745"],"schema_version":"newruntime-agent-readable-v0.2","stable_id":"post:proxyattn-a-training-free-method-from-researchers-at-59312c","status":"published","visuals":[],"editorial_provenance":{"schema_version":"newruntime-editorial-copy-v1","content_status":"source_grounded_final","final_copy_sha256":"sha256:a22bdc48ed0633a46ef923bcfb607354d179d82cfacb253000d4aeca058ae726","reviewed_at":"2026-10-15T07:01:32.401Z","source_evidence_count":1,"verified_claim_count":3,"site_analysis_schema_version":"newruntime-site-analysis-v1","site_object_kind":"field_note","observed_fact_count":3,"implication_count":2,"watch_condition_count":1,"related_record_count":0},"analysis":{"schema_version":"newruntime-site-analysis-v1","object_kind":"field_note","thesis":"proxy-head scores can guide sparse attention while dynamic budgets adapt the number of selected blocks to each head.","observed_facts":[{"text":"The arXiv abstract describes ProxyAttn as a training-free sparse attention algorithm that uses pooled representative heads to approximate scores for all heads.","source_urls":["https://arxiv.org/abs/2509.24745"]},{"text":"The abstract reports up to 10.3× attention acceleration and 2.4× prefill acceleration without significant performance loss.","source_urls":["https://arxiv.org/abs/2509.24745"]},{"text":"The arXiv page identifies the paper as an ICLR 2026 camera-ready version and states that code is available.","source_urls":["https://arxiv.org/abs/2509.24745"]}],"mechanism":"representative heads provide a lower-cost signal for block importance, while head-specific budgets address variation in sparsity needs.","why_now":"long-context prefill remains costly because attention has quadratic complexity, and the paper positions dynamic block selection as a way to reduce that burden.","implications":["the method makes representative-head selection and per-head budget behavior important implementation and evaluation variables.","Builders should compare quality and end-to-end prefill latency rather than infer deployment gains from kernel speed alone."],"evidence_boundary":"these are maxima reported by the authors across their evaluations, not independent confirmation or a guarantee for every model, sequence length, hardware setup, or sparsity target.","watch_conditions":["whether the released implementation reproduces the reported speedups across models and realistic long-context workloads."],"related_records":[],"new_branch_reason":"No prior New Runtime analytical record was supplied; this creates a distinct record on representative-head-guided sparse attention."},"editor":{"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]},"routes":{"html":"https://newruntime.com/posts/proxyattn-a-training-free-method-from-researchers-at-59312c/","markdown":"https://newruntime.com/posts/proxyattn-a-training-free-method-from-researchers-at-59312c.md","json":"https://newruntime.com/posts/proxyattn-a-training-free-method-from-researchers-at-59312c.json"},"next_reads":[{"type":"topic","path":"/topics/models/","url":"https://newruntime.com/topics/models/","title":"Models","reason":"Explore the models topic hub.","media_type":"text/html","markdown_url":"https://newruntime.com/topics/models.md","json_url":"https://newruntime.com/topics/models.json"},{"type":"topic","path":"/topics/inference/","url":"https://newruntime.com/topics/inference/","title":"Inference","reason":"Explore the inference topic hub.","media_type":"text/html","markdown_url":"https://newruntime.com/topics/inference.md","json_url":"https://newruntime.com/topics/inference.json"},{"type":"collection","path":"/posts/","url":"https://newruntime.com/posts/","title":"Field Notes","reason":"Open the latest editorial analysis.","media_type":"text/html","json_url":"https://newruntime.com/posts.json"},{"type":"collection","path":"/source-ledger.json","url":"https://newruntime.com/source-ledger.json","title":"Source Ledger","reason":"Inspect the public source evidence graph.","media_type":"application/json"}]}