{"id":"https://openalex.org/W7169495353","doi":"https://doi.org/10.48550/arxiv.2607.14149","title":"Enhancing Small Language Models Reasoning through Knowledge Graph Grounding","display_name":"Enhancing Small Language Models Reasoning through Knowledge Graph Grounding","publication_year":2026,"publication_date":"2026-07-14","ids":{"openalex":"https://openalex.org/W7169495353","doi":"https://doi.org/10.48550/arxiv.2607.14149"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.14149","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.14149","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.2607.14149","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077986639","display_name":"Dimitrios Kelesis","orcid":"https://orcid.org/0000-0002-3434-2717"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kelesis, Dimitrios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069071383","display_name":"Konstantinos Bougiatiotis","orcid":"https://orcid.org/0000-0002-1910-2758"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bougiatiotis, Konstantinos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010395244","display_name":"\u0393\u03b5\u03ce\u03c1\u03b3\u03b9\u03bf\u03c2 \u03a0\u03b1\u03bb\u03b9\u03bf\u03cd\u03c1\u03b1\u03c2","orcid":"https://orcid.org/0000-0001-9629-2367"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paliouras, Georgios","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":0.41280001401901245,"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":0.41280001401901245,"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":0.25949999690055847,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.14499999582767487,"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/bottleneck","display_name":"Bottleneck","score":0.6359999775886536},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.4993000030517578},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41449999809265137},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.40959998965263367},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.37400001287460327},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.3677000105381012},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.32120001316070557},{"id":"https://openalex.org/keywords/expert-system","display_name":"Expert system","score":0.31200000643730164},{"id":"https://openalex.org/keywords/model-checking","display_name":"Model checking","score":0.30820000171661377}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7786999940872192},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6359999775886536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5101000070571899},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.4993000030517578},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41449999809265137},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.40959998965263367},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.37400001287460327},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33809998631477356},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33329999446868896},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C110251889","wikidata":"https://www.wikidata.org/wiki/Q1569697","display_name":"Model checking","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.29899999499320984},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.29330000281333923},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C164155591","wikidata":"https://www.wikidata.org/wiki/Q2067766","display_name":"Satisfiability modulo theories","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C179603123","wikidata":"https://www.wikidata.org/wiki/Q1941921","display_name":"Modeling language","level":3,"score":0.2750999927520752},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.2581000030040741},{"id":"https://openalex.org/C168993435","wikidata":"https://www.wikidata.org/wiki/Q6501125","display_name":"Ground","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25619998574256897},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.14149","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.14149","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.2607.14149","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.14149","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Although":[0],"large":[1],"language":[2],"models":[3,98],"(LLMs)":[4],"have":[5],"set":[6],"benchmarks":[7],"for":[8,79,85,192],"zero-shot":[9],"reasoning,":[10],"their":[11],"deployment":[12],"remains":[13],"cost-prohibitive":[14],"and":[15,55,83,110,143,188],"environmentally":[16],"taxing.":[17],"Small":[18],"Language":[19],"Models":[20],"(SLMs)":[21],"offer":[22],"a":[23,39,70,89,111,126,158,190],"sustainable":[24],"alternative,":[25],"but":[26],"prone":[27],"to":[28,43],"errors,":[29],"on":[30,115],"tasks":[31],"requiring":[32],"complex,":[33],"multi-hop":[34,153],"logical":[35],"grounding.":[36],"We":[37,95],"investigate":[38],"neuro-symbolic":[40,196],"agentic":[41,186,197],"framework":[42],"enhance":[44],"the":[45,60,67,135,140,171,179],"reasoning":[46,87],"capabilities":[47],"of":[48,173,181],"SLMs,":[49],"specifically":[50],"Gemma":[51],"3":[52],"(1B,":[53],"4B)":[54],"Llama":[56],"3.2":[57],"(3B),":[58],"using":[59],"CLUTRR":[61],"kinship":[62],"benchmark.":[63],"Our":[64,118],"approach":[65],"transforms":[66],"SLM":[68],"into":[69],"minimalist":[71],"agent":[72],"utilizing":[73],"two":[74,100],"specialized":[75],"tool":[76],"calls:":[77],"extract_facts":[78],"symbolic":[80,182],"triplet":[81],"extraction":[82,141,149],"get_hint":[84],"expert":[86,174],"via":[88],"Relational":[90],"Graph":[91],"Convolutional":[92],"Network":[93],"(RGCN).":[94],"evaluate":[96],"these":[97],"across":[99],"configurations,":[101],"both":[102],"in":[103,161,184,195],"an":[104],"Oracle":[105],"scenario":[106,113],"with":[107],"ground-truth":[108],"triplets":[109],"Realistic":[112],"relying":[114],"self-extracted":[116],"knowledge.":[117],"results":[119],"reveal":[120],"that":[121],"while":[122],"RGCN-derived":[123],"hints":[124],"provide":[125],"1.5":[127],"-":[128],"2x":[129],"performance":[130,169],"gain":[131],"over":[132,152],"story-only":[133],"baselines,":[134],"system":[136],"is":[137],"constrained":[138],"by":[139],"bottleneck":[142],"sequential":[144],"deductive":[145],"fragility,":[146],"where":[147,164],"early":[148],"errors":[150],"compound":[151],"chains.":[154],"Furthermore,":[155],"we":[156],"identify":[157],"\"distraction":[159],"effect\"":[160],"specific":[162],"architectures":[163],"noisy,":[165],"self-generated":[166],"facts":[167],"degrade":[168],"despite":[170],"presence":[172],"hints.":[175],"This":[176],"work":[177],"characterizes":[178],"challenges":[180],"grounding":[183],"low-resource":[185],"systems":[187],"provides":[189],"roadmap":[191],"iterative":[193],"verification":[194],"pipelines.":[198]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-18T00:00:00"}