{"id":"https://openalex.org/W7204944516","doi":"https://doi.org/10.48550/arxiv.2608.31066","title":"Every Token Leaves a Ripple in the Stream of Thought: Eliciting Model-Internal Token Saliency for Chain-of-Thought Compression","display_name":"Every Token Leaves a Ripple in the Stream of Thought: Eliciting Model-Internal Token Saliency for Chain-of-Thought Compression","publication_year":2026,"publication_date":"2026-08-31","ids":{"openalex":"https://openalex.org/W7204944516","doi":"https://doi.org/10.48550/arxiv.2608.31066"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2608.31066","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.31066","pdf_url":"https://arxiv.org/pdf/2608.31066","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.31066","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148910647","display_name":"Tianyi Zhao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhao, Tianyi","raw_affiliation_strings":["University of Virginia {abs4dj ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Virginia {abs4dj ,","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148867961","display_name":"Yinhan He","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"He, Yinhan","raw_affiliation_strings":["University of Virginia {abs4dj ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Virginia {abs4dj ,","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148902443","display_name":"Wendy Zheng","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zheng, Wendy","raw_affiliation_strings":["University of Virginia {abs4dj ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Virginia {abs4dj ,","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5157661584","display_name":"Jundong Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li, Jundong","raw_affiliation_strings":["University of Virginia {abs4dj ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Virginia {abs4dj ,","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5157739697","display_name":"Chen Chen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen, Chen","raw_affiliation_strings":["University of Virginia {abs4dj ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Virginia {abs4dj ,","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.8047000169754028,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.8047000169754028,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.13940000534057617,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.007899999618530273,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/chain-of-thought-reasoning","display_name":"chain-of-thought reasoning","score":0.8859999775886536},{"id":"https://openalex.org/keywords/residual-stream","display_name":"residual stream","score":0.7289999723434448},{"id":"https://openalex.org/keywords/inference-cost-reduction","display_name":"inference cost reduction","score":0.7039999961853027},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.6050000190734863}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.8529999852180481},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7002999782562256},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5591999888420105},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.5333999991416931},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5231000185012817},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.48590001463890076},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46399998664855957},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.4526999890804291},{"id":"https://openalex.org/C2779599953","wikidata":"https://www.wikidata.org/wiki/Q1776117","display_name":"Ripple","level":3,"score":0.3774999976158142},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C115067241","wikidata":"https://www.wikidata.org/wiki/Q1639854","display_name":"Token passing","level":3,"score":0.3515999913215637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32109999656677246},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.31066","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.31066","pdf_url":"https://arxiv.org/pdf/2608.31066","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.31066","doi":"https://doi.org/10.48550/arxiv.2608.31066","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.31066","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.31066","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.31066","pdf_url":"https://arxiv.org/pdf/2608.31066","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204944516.pdf","grobid_xml":"https://content.openalex.org/works/W7204944516.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7166806920","https://openalex.org/W6929531631","https://openalex.org/W7126081047","https://openalex.org/W4417102263","https://openalex.org/W4405562786","https://openalex.org/W7134838655","https://openalex.org/W4415192111","https://openalex.org/W7139080523","https://openalex.org/W7104972725","https://openalex.org/W7172329997"],"abstract_inverted_index":{"Chain-of-thought":[0],"(CoT)":[1],"reasoning":[2,9,23,69,150],"improves":[3],"multi-step":[4],"problem":[5],"solving,":[6],"but":[7],"long":[8],"traces":[10,27],"inflate":[11],"inference":[12],"cost.":[13],"Token-level":[14,101],"CoT":[15,102],"compression":[16],"reduces":[17],"this":[18,93],"cost":[19],"by":[20],"pruning":[21],"full":[22],"chains":[24],"into":[25],"shorter":[26],"for":[28,100,146,168],"model":[29,64],"adaptation,":[30],"making":[31],"token":[32,70,106],"selection":[33],"the":[34,51,63,75,81,84,88,113,127,139],"central":[35],"challenge.":[36],"Existing":[37],"methods":[38],"often":[39],"rely":[40],"on":[41,80,92],"external":[42],"scorers":[43],"or":[44],"heuristic":[45],"signals":[46],"only":[47],"indirectly":[48],"tied":[49],"to":[50,87],"model's":[52],"internal":[53,121],"answer":[54,82,116,130],"computation.":[55,90],"We":[56],"instead":[57],"adopt":[58],"a":[59,72,119,142],"model-internal":[60,162],"perspective:":[61],"as":[62],"forms":[65],"an":[66,165],"answer,":[67],"each":[68],"induces":[71],"ripple":[73],"in":[74,115,129],"residual":[76],"stream":[77],"whose":[78],"effect":[79],"reflects":[83],"token's":[85,120],"contribution":[86,122,134],"underlying":[89],"Building":[91],"view,":[94],"we":[95],"propose":[96],"\\textsc{MIST}":[97,155],"(Model-Internal":[98],"Saliency":[99],"compression),":[103],"which":[104],"defines":[105],"importance":[107,144],"along":[108],"two":[109,140],"complementary":[110],"axes:":[111],"\\emph{necessity},":[112],"drop":[114],"likelihood":[117,131],"when":[118,132],"is":[123,136],"removed,":[124],"and":[125,152],"\\emph{sufficiency},":[126],"gain":[128],"that":[133,161],"alone":[135],"provided.":[137],"Combining":[138],"yields":[141],"unified":[143],"score":[145],"pruning.":[147],"Across":[148],"four":[149,153],"benchmarks":[151],"models,":[154],"consistently":[156],"outperforms":[157],"baseline":[158],"methods,":[159],"suggesting":[160],"saliency":[163],"provides":[164],"effective":[166],"proxy":[167],"reasoning-token":[169],"importance.":[170]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-09-02T00:00:00"}
