{"id":"https://openalex.org/W7163073443","doi":"https://doi.org/10.48550/arxiv.2605.31308","title":"TraceGraph: Shared Decision Landscapes for Diagnosing and Improving Agent Trajectories","display_name":"TraceGraph: Shared Decision Landscapes for Diagnosing and Improving Agent Trajectories","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163073443","doi":"https://doi.org/10.48550/arxiv.2605.31308"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.31308","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31308","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.31308","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122980572","display_name":"Junjie Nian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nian, Junjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137543389","display_name":"Kang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Kang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137569958","display_name":"Ge Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ge","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137609850","display_name":"Yixin Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Yixin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137600160","display_name":"Yugang Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Yugang","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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.23549999296665192,"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":0.23549999296665192,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.2329999953508377,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.09000000357627869,"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/vocabulary","display_name":"Vocabulary","score":0.5764999985694885},{"id":"https://openalex.org/keywords/trap","display_name":"Trap (plumbing)","score":0.5626000165939331},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5236999988555908},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.49810001254081726},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.48489999771118164},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4406999945640564},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4307999908924103},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.382999986410141},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.3709999918937683}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6093999743461609},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.5764999985694885},{"id":"https://openalex.org/C121099081","wikidata":"https://www.wikidata.org/wiki/Q665580","display_name":"Trap (plumbing)","level":2,"score":0.5626000165939331},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5236999988555908},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.49810001254081726},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.48489999771118164},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4406999945640564},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.382999986410141},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.3707999885082245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.362199991941452},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C2778355321","wikidata":"https://www.wikidata.org/wiki/Q17079427","display_name":"Identity (music)","level":2,"score":0.32600000500679016},{"id":"https://openalex.org/C2776672683","wikidata":"https://www.wikidata.org/wiki/Q1444953","display_name":"Poverty trap","level":3,"score":0.30559998750686646},{"id":"https://openalex.org/C88626702","wikidata":"https://www.wikidata.org/wiki/Q1128903","display_name":"Continuation","level":2,"score":0.3034000098705292},{"id":"https://openalex.org/C141141315","wikidata":"https://www.wikidata.org/wiki/Q2379942","display_name":"Guard (computer science)","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.2924000024795532},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2799000144004822},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.25040000677108765},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.31308","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31308","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.31308","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31308","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.5357603430747986}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Agent":[0],"benchmarks":[1,180],"increasingly":[2],"record":[3],"rich":[4],"interaction":[5],"trajectories,":[6],"yet":[7],"evaluation":[8],"often":[9],"reduces":[10],"each":[11,37,66],"rollout":[12,67],"to":[13,151,161],"a":[14,23,41,112,173,186],"pass":[15],"rate":[16,148],"or":[17,102],"reward":[18,99],"score.":[19],"We":[20],"introduce":[21],"TraceGraph,":[22],"graph-based":[24],"framework":[25],"that":[26,93],"turns":[27],"released":[28],"multi-model":[29],"agent":[30,179],"trajectories":[31,77],"into":[32],"shared":[33,187],"decision":[34],"landscapes.":[35],"For":[36],"task,":[38],"TraceGraph":[39,82,108,171],"builds":[40],"graph":[42],"over":[43],"observable":[44],"action-observation":[45],"states":[46,122],"from":[47,104,133,149,159],"pooled":[48,142],"rollouts":[49],"before":[50],"model":[51],"identity":[52],"is":[53],"introduced.":[54],"It":[55],"then":[56,127],"overlays":[57],"outcome-informed":[58],"productive":[59],"cores":[60],"and":[61,64,74,91,158,189],"trap":[62,125],"regions,":[63,126],"summarizes":[65],"with":[68,166],"three":[69],"events:":[70],"Access,":[71],"Trap":[72],"exposure,":[73],"Repair.":[75],"Across":[76],"spanning":[78],"five":[79],"benchmark":[80],"splits,":[81],"profiles":[83],"reveal":[84],"navigation":[85],"differences":[86],"hidden":[87],"by":[88],"aggregate":[89],"scores":[90],"show":[92],"splits":[94],"differ":[95],"in":[96],"whether":[97],"they":[98],"avoiding":[100],"traps":[101],"recovering":[103],"them.":[105],"The":[106],"same":[107,135],"landscape":[109],"also":[110],"motivates":[111],"trap-aware":[113],"recovery":[114],"pipeline":[115],"for":[116,176],"SWE-bench:":[117],"aruntime":[118],"detector":[119],"fires":[120],"on":[121,153,163,185],"matching":[123],"historical":[124],"lightweight":[128],"continuation":[129],"policies":[130],"are":[131],"evaluated":[132],"the":[134,140,154],"prefix.":[136],"On":[137],"fired":[138,156],"states,":[139],"best":[141],"single-factor":[143],"policy":[144],"raises":[145],"official":[146],"resolved":[147],"40.4%":[150],"43.5%":[152],"per-provider":[155],"subset":[157],"41.0%":[160],"44.8%":[162],"common-fired":[164],"instances,":[165],"provider-specific":[167],"active":[168],"components.":[169],"Overall,":[170],"provides":[172],"process":[174],"vocabulary":[175],"asking":[177],"what":[178],"test,":[181],"where":[182],"models":[183],"diverge":[184],"landscape,":[188],"how":[190],"failure":[191],"regions":[192],"can":[193],"guide":[194],"downstream":[195],"improvement.":[196]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-02T00:00:00"}