{"paper":{"title":"Stability Implies Redundancy: Delta Attention Selective Halting for Efficient Long-Context Prefilling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Tokens reach semantic fixing points where further attention adds nothing, so early halting cuts prefill cost.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Linfeng Zhang, Shaobo Wang, Tailai Chen, Yifeng Gao, Yijue Xu, Yujie Chen, Zoe Wanying He","submitted_at":"2026-04-20T11:20:03Z","abstract_excerpt":"Prefilling computational costs pose a significant bottleneck for Large Language Models (LLMs) and Large Multimodal Models (LMMs) in long-context settings. While token pruning reduces sequence length, prior methods rely on heuristics that break compatibility with hardware-efficient kernels like FlashAttention. In this work, we observe that tokens evolve toward \\textit{semantic fixing points}, making further processing redundant. To this end, we introduce Delta Attention Selective Halting (DASH), a training-free policy that monitors the layer-wise update dynamics of the self-attention mechanism "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Tokens evolve toward semantic fixing points, making further processing redundant; DASH monitors layer-wise update dynamics of self-attention to selectively halt stabilized tokens and delivers significant prefill speedups while preserving accuracy and hardware efficiency.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That monitoring layer-wise self-attention deltas reliably identifies tokens whose further computation is redundant without discarding task-critical information, and that this holds across models and tasks without per-model tuning.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DASH selectively halts stabilized tokens by monitoring layer-wise self-attention deltas, delivering prefill speedups on language and vision tasks without accuracy loss or hardware incompatibility.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Tokens reach semantic fixing points where further attention adds nothing, so early halting cuts prefill cost.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9d25ec1061c50d9d1d8d50cb162bf4d9174cf6f11f8eafdd068f7cb3d51cbc90"},"source":{"id":"2604.18103","kind":"arxiv","version":2},"verdict":{"id":"716d62aa-0f78-449b-b9c6-f09643d52732","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T04:37:00.311740Z","strongest_claim":"Tokens evolve toward semantic fixing points, making further processing redundant; DASH monitors layer-wise update dynamics of self-attention to selectively halt stabilized tokens and delivers significant prefill speedups while preserving accuracy and hardware efficiency.","one_line_summary":"DASH selectively halts stabilized tokens by monitoring layer-wise self-attention deltas, delivering prefill speedups on language and vision tasks without accuracy loss or hardware incompatibility.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That monitoring layer-wise self-attention deltas reliably identifies tokens whose further computation is redundant without discarding task-critical information, and that this holds across models and tasks without per-model tuning.","pith_extraction_headline":"Tokens reach semantic fixing points where further attention adds nothing, so early halting cuts prefill cost."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.18103/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-20T04:26:08.555427Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"33e06639ba6363a14fc96f891c03f2d45d6032ca657c88c6b4dab87e38ff7a2f"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}