{"data":{"event":{"id":"86d77a9b-d047-490f-97ed-7b20e77b0627","slug":"learning-symbolic-constraint-representations-from-examples-a-neuro-symbo-1e755da9a8","title":"Learning Symbolic Constraint Representations from Examples: A Neuro-Symbolic Approach","short_summary":"arXiv:2609.12267v1 Announce Type: new \nAbstract: Learning user-defined concepts as constraint networks has been extensively studied in the constraint acquisition (CA) literature. However, existing approaches typically rely on intensive interactions with a human oracle, making the learning process costly in terms of time and number of queries. In this paper, we propose a neuro-symbolic framework for automatic CA that significantly reduces user involvement by introducing neural Oracle Transformer models which learn to emulate user responses and to generalize conceptual knowledge. Trained on prev","full_description":"arXiv:2609.12267v1 Announce Type: new \nAbstract: Learning user-defined concepts as constraint networks has been extensively studied in the constraint acquisition (CA) literature. However, existing approaches typically rely on intensive interactions with a human oracle, making the learning process costly in terms of time and number of queries. In this paper, we propose a neuro-symbolic framework for automatic CA that significantly reduces user involvement by introducing neural Oracle Transformer models which learn to emulate user responses and to generalize conceptual knowledge. Trained on previously available examples, the learned oracle interacts with a dedicated CA engine, FastCA, which systematically refines the oracle's responses into a sound, consistent, and interpretable constraint network. This neuro-symbolic interaction enables the recovery of structured symbolic models from data without prior domain knowledge. Our results demonstrate that this neuro-symbolic interplay effectively aligns data-driven pattern recognition with symbolic reasoning, offering a robust approach to automating model construction in combinatorial domains.\n\nUpdate (2026-09-22): Generative Embodied Multiple Behavior Control Systems for Human-like Agents. arXiv:2609.22691v1 Announce Type: new \nAbstract: An enduring and richly elaborated dichotomy in cognitive neuroscience is that of human behavior control mechanisms, divided into habitual versus goal-directed. While existing human-like agent frameworks primarily focus on modeling goal- directed behavior, habitual behavior has been largely overlooked, though it plays a crucial role in human daily life. In this paper, we address this gap by studying multiple behavior control systems that jointly model goal-directed and habitual behaviors. We propose a human behavior control mechanism-inspired framework which the Habitual Controller retrieves cue-triggered behaviors from personal- ized habit memory, while the Goal-directed Controller employs a context-aware world model to predict action consequences and estimate their values. The Arbiter dynamically balances the influence of both systems according to individual differ- ences and momentary internal states. To reconstruct diverse human-level behavior instructions in 3D environments, we further develop a keyframe-guided 3D mo- tion generati","ledger_type":"benefit","primary_domain_id":"8f1af1b9-7302-4b51-aae9-fdfac04d158a","event_status":"provisional","event_date":"2026-09-14T00:00:00.000Z","discovery_date":"2026-09-14T00:00:00.000Z","first_published_date":"2026-09-14T00:00:00.000Z","last_reviewed_date":"2026-09-22T00:00:00.000Z","geographic_scope":"International","affected_population":null,"base_impact_tier":1,"base_score":"1.00","attribution_multiplier":"0.1000","evidence_multiplier":"0.2000","realization_multiplier":"0.2000","durability_multiplier":"0.5000","current_event_score":"0.002000","confidence_level":"low","score_explanation":"Ingest update: independent source raised evidence; added a follow-up source. Score remains provisional until review.","methodology_version_id":"d7881163-fb23-4e71-8625-bac1a8662c0f","original_methodology_version_id":"d7881163-fb23-4e71-8625-bac1a8662c0f","published_at":"2026-09-14T04:00:51.561Z","created_at":"2026-09-14T04:00:51.561Z","updated_at":"2026-09-23T03:01:08.070Z","flags":[],"domain_name":"Biology","domain_slug":"biology","methodology_version":"0.1"},"contributions":[{"id":"be0d9bee-830b-44c9-87f4-5738b20cfeef","event_id":"86d77a9b-d047-490f-97ed-7b20e77b0627","model_id":"535014f7-c941-4d39-aa9b-8f9c1d3d07b7","role_description":"Unspecified system mentioned or implied by a news item. Remap to a named release when identified.","attribution_multiplier":"0.1000","credit_share":"1.0000","contribution_score":"0.002000","attribution_rationale":"News ingest does not infer a named model from the publisher alone. Attribution stays unspecified until a release is identified.","attribution_confidence":"medium","first_used_date":"2026-09-14T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-14T04:00:51.570Z","model_slug":"unspecified-ai-system","model_name":"Unspecified AI system","identity_class":"unknown","is_internal":false,"family_name":"Unspecified","family_slug":"unknown-unspecified","organization_name":"Unknown","organization_slug":"unknown"}],"sources":[{"id":"f7c0178a-1241-44fe-bee1-d3e5e7bb3db4","event_id":"86d77a9b-d047-490f-97ed-7b20e77b0627","url":"https://arxiv.org/abs/2609.12267","canonical_url":"https://arxiv.org/abs/2609.12267","source_type":"preprint","publisher":"arXiv cs.AI","author":null,"publication_date":"2026-09-14T00:00:00.000Z","retrieved_at":"2026-09-14T04:00:51.576Z","title":"Learning Symbolic Constraint Representations from Examples: A Neuro-Symbolic Approach","excerpt":"arXiv:2609.12267v1 Announce Type: new \nAbstract: Learning user-defined concepts as constraint networks has been extensively studied in the constraint acquisition (CA) literature. However, existing approaches typically rely on intensive interactions with a human oracle, making the learning process costly in terms of time and number of queries. In this paper, we propose a neuro-symbolic framework for automatic CA that significantly reduces user involvement by introducing neural Oracle Transformer ","content_hash":null,"source_reliability_class":"medium","is_primary_source":true,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-14T04:00:51.576Z"},{"id":"ebe127da-0bde-4d1f-a9d8-a71f17b06079","event_id":"86d77a9b-d047-490f-97ed-7b20e77b0627","url":"https://arxiv.org/abs/2609.22691","canonical_url":"https://arxiv.org/abs/2609.22691","source_type":"preprint","publisher":"arXiv cs.AI","author":null,"publication_date":"2026-09-22T00:00:00.000Z","retrieved_at":"2026-09-23T03:01:08.054Z","title":"Generative Embodied Multiple Behavior Control Systems for Human-like Agents","excerpt":"arXiv:2609.22691v1 Announce Type: new \nAbstract: An enduring and richly elaborated dichotomy in cognitive neuroscience is that of human behavior control mechanisms, divided into habitual versus goal-directed. While existing human-like agent frameworks primarily focus on modeling goal- directed behavior, habitual behavior has been largely overlooked, though it plays a crucial role in human daily life. In this paper, we address this gap by studying multiple behavior control systems that jointly mo","content_hash":null,"source_reliability_class":"medium","is_primary_source":false,"is_independent":false,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-23T03:01:08.054Z"}],"claims":[{"id":"15b921cf-5ba5-4b10-a6cd-f3959368634d","event_id":"86d77a9b-d047-490f-97ed-7b20e77b0627","claim_text":"Learning Symbolic Constraint Representations from Examples: A Neuro-Symbolic Approach","claim_type":"outcome","claim_status":"supported","confidence_score":null,"created_at":"2026-09-14T04:00:51.584Z","updated_at":"2026-09-14T04:00:51.584Z"}],"revisions":[{"id":"39111433-e22a-41cf-8a93-d38b4dacc1c1","event_id":"86d77a9b-d047-490f-97ed-7b20e77b0627","model_id":null,"previous_score":"0.000000","new_score":"0.001000","previous_factors":{},"new_factors":{"evidence":0.1,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Auto-published from news ingest.","trigger_type":"news_ingest","trigger_source_ids":null,"reviewer_id":null,"review_status":"published","created_at":"2026-09-14T04:00:51.591Z"},{"id":"d4f92e52-fd7e-4698-9934-38b52905a2aa","event_id":"86d77a9b-d047-490f-97ed-7b20e77b0627","model_id":null,"previous_score":"0.001000","new_score":"0.002000","previous_factors":{"tier":1,"evidence":0.1,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"new_factors":{"tier":1,"models":["535014f7-c941-4d39-aa9b-8f9c1d3d07b7"],"evidence":0.2,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Ingest update: independent source raised evidence; added a follow-up source.","trigger_type":"ingest_update","trigger_source_ids":["ebe127da-0bde-4d1f-a9d8-a71f17b06079"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-23T03:01:08.082Z"}],"secondary":[]},"methodology_version":"0.1"}