{"id":"https://openalex.org/W7164207252","doi":"https://doi.org/10.48550/arxiv.2606.11172","title":"Predicting Future Behaviors in Reasoning Models Enables Better Steering","display_name":"Predicting Future Behaviors in Reasoning Models Enables Better Steering","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164207252","doi":"https://doi.org/10.48550/arxiv.2606.11172"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.11172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11172","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,"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.11172","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138327461","display_name":"Evgenii Kortukov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kortukov, Evgenii","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138368715","display_name":"Piotr Komorowski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Komorowski, Piotr","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138351876","display_name":"Florian Klein","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Klein, Florian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138356841","display_name":"Paula Engl","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Engl, Paula","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138296001","display_name":"Gabriele Sarti","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sarti, Gabriele","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138300882","display_name":"Seong Joon Oh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oh, Seong Joon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138331594","display_name":"Sebastian Lapuschkin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lapuschkin, Sebastian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138338009","display_name":"Wojciech Samek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Samek, Wojciech","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T10028","display_name":"Topic Modeling","score":0.6211000084877014,"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/T10028","display_name":"Topic Modeling","score":0.6211000084877014,"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":0.11509999632835388,"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"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.07000000029802322,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3896999955177307},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.3885999917984009},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.3176000118255615},{"id":"https://openalex.org/keywords/internal-model","display_name":"Internal model","score":0.3070000112056732},{"id":"https://openalex.org/keywords/multiple-models","display_name":"Multiple Models","score":0.3046000003814697}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6651999950408936},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.574400007724762},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3896999955177307},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3772999942302704},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C28427503","wikidata":"https://www.wikidata.org/wiki/Q13580300","display_name":"Internal model","level":3,"score":0.3070000112056732},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C2989277270","wikidata":"https://www.wikidata.org/wiki/Q168338","display_name":"Behavioral analysis","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.28049999475479126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.11172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11172","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,"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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.11172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.11172","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,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deployed":[0],"large":[1],"reasoning":[2,75],"models":[3],"(LRMs)":[4],"often":[5],"behave":[6],"unexpectedly.":[7],"Test-time":[8],"steering":[9,29,104,131,141,147],"controls":[10],"LRM":[11,165],"outputs":[12],"by":[13],"intervening":[14],"on":[15,33,96],"their":[16],"hidden":[17],"representations,":[18],"but":[19],"it":[20],"can":[21],"degrade":[22],"output":[23,135],"quality.":[24],"We":[25,43],"argue":[26],"that":[27,36,45,152],"prior":[28],"work":[30],"implicitly":[31],"relies":[32],"internal":[34,92],"features":[35,48,157],"detect":[37],"behavior":[38,71,83,127],"in":[39,142],"already":[40],"generated":[41],"text.":[42],"show":[44,151],"these":[46,97],"detection":[47,154],"are":[49],"poor":[50],"predictors":[51],"of":[52,91],"future":[53,70,126],"behavioral":[54],"outcomes,":[55],"and":[56,115,155],"thus":[57],"not":[58],"the":[59,80,117,125],"natural":[60],"intervention":[61],"target.":[62],"Instead,":[63],"we":[64,100],"train":[65],"activation":[66,146],"probes":[67,78],"to":[68,121,163],"predict":[69,79],"likelihoods":[72],"from":[73],"intermediate":[74],"steps.":[76],"These":[77,149],"most":[81],"likely":[82],"with":[84,132],"64%-91%":[85],"accuracy,":[86],"revealing":[87],"a":[88,102,122,159],"separate":[89],"type":[90],"prediction":[93,98,156],"features.":[94],"Building":[95],"features,":[99],"introduce":[101],"text-level":[103],"method,":[105],"Future":[106],"Probe":[107],"Controlled":[108],"Generation.":[109],"FPCG":[110,138],"samples":[111],"multiple":[112],"candidate":[113],"sentences":[114],"chooses":[116],"best":[118],"one":[119],"according":[120],"probe":[123],"predicting":[124],"likelihood.":[128],"This":[129],"enables":[130,140,158],"almost":[133],"no":[134],"quality":[136],"degradation.":[137],"also":[139],"several":[143],"evaluations":[144],"where":[145],"fails.":[148],"results":[150],"distinguishing":[153],"more":[160],"nuanced":[161],"approach":[162],"controlling":[164],"behaviors.":[166]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-11T00:00:00"}
