{"total":10,"items":[{"citing_arxiv_id":"2605.21951","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Dynamic Mixture of Latent Memories for Self-Evolving Agents","primary_cat":"cs.LG","submitted_at":"2026-05-21T03:35:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MoLEM achieves a 10.40% average accuracy improvement in continual learning tasks across math, science, and code by using dynamic latent memory experts with a frozen base model and stage-specific autoencoders for routing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20075","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning","primary_cat":"cs.CL","submitted_at":"2026-05-19T16:28:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"CopT reverses CoT by eliciting a draft answer first then using continuous-embedding contrastive verification and on-policy thinking to reflect and correct, yielding up to 23% higher accuracy and 57% fewer tokens without training.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06165","ref_index":90,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost","primary_cat":"cs.AI","submitted_at":"2026-05-07T12:51:49+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21027","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering","primary_cat":"cs.AI","submitted_at":"2026-04-22T19:18:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"We discretize counts into {0,1, . . . , Kmax}, where Kmax is chosen based on the empirical distribution (e.g., a high percentile). The head outputs logitso∈R Kmax+1: o= MLP count(hcount), p= softmax(o),(22) where pk denotes the predicted probability of count k. Loss.For a ground-truth count k (clipped to Kmax if necessary), we use cross-entropy: Lcount =−logp k .(23) When the original answer is an empty array for a count-type question, we normalize it to k= 0 during preprocessing. D.1.5 Overall Objective For each question, exactly one head is activated based on the parsed answer type. The total QA loss aggregates the head-specific losses together with auxiliary pretraining and geometry-aware reg- ularization terms, where only the relevant terms"},{"citing_arxiv_id":"2604.08299","ref_index":50,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SeLaR: Selective Latent Reasoning in Large Language Models","primary_cat":"cs.CL","submitted_at":"2026-04-09T14:32:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SeLaR selectively applies latent soft reasoning in LLMs via entropy gating and contrastive regularization, outperforming standard CoT on five benchmarks without training.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.17837","ref_index":37,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning","primary_cat":"eess.AS","submitted_at":"2026-03-18T15:30:29+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.08324","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression","primary_cat":"cs.LG","submitted_at":"2026-02-09T06:57:15+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.06803","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Forest Before Trees: Latent Superposition for Efficient Visual Reasoning","primary_cat":"cs.CL","submitted_at":"2026-01-11T08:30:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Laser reformulates visual reasoning via Dynamic Windowed Alignment Learning to maintain latent superposition of global features, delivering 5.03% average gains over Monet and over 97% fewer inference tokens on six benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.19917","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models","primary_cat":"cs.CL","submitted_at":"2026-01-07T12:38:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PILOT internalizes strategic planning into compact LLMs by using a hyper-network to generate query-conditioned latent guidance vectors that stabilize reasoning trajectories and improve benchmark performance with negligible added latency.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2503.16419","ref_index":206,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models","primary_cat":"cs.CL","submitted_at":"2025-03-20T17:59:38+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A survey organizing techniques to achieve efficient reasoning in LLMs by shortening chain-of-thought outputs.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Distilling 2-1 [219]; C3oT [78]; TokenSkip [194]; CoT-Valve [130]; Self-Training [133]; Learnto Skip [115]; Token-Budget [58]; Verbosity [72]; Stepwise [31]; Z1 [223]; Prune-on-Logic [243];LS-Mixture SFT [218]; DRP [75]; AutoL2S [125]; Assembly of Experts [79]; Ada-R1 [126];ConCISE [145]; VeriThinker [19]; R1-Compress [187]; CTS [226]; A∗-Thought [205]; TLDR [96];OThink-R1 [235]; PNS [220]; ReCUT [77]; StepEntropy [94]; ASAP [229]; ReasoningOutput-basedEfficient Reasoning LatentRepresentationCompression e.g. Coconut [60]; CODI [155]; CCoT [20]; Heima [153]; Token Assorted [163]; Loop [149];SoftCoT [206]; Back Attention [222]; CoLaR [167]; SEAL [14]; Overclocking [41];Controlling [106]; DynamicReasoningParadigm e.g. Speculative Rejection [165]; Sampling-Efficient TTS [188]; DPTS [38]; Certaindex [50];Dynasor-CoT [51]; Fast MCTS [89]; ST-BoN [188]; More is Less [193]; RSD [100]; SpeculativeThinking [213]; SCoT [181]; SpecSearch [189]; ValueFree [148]; GG [54]; VGS [183]; DO [61];FFS [1]; DORA [185]; SPECS [10]; Best-Route [37]; LightThinker [231]; INFTYTHINK [209];SCoT [195]; RASC [178]; Adaptive Reasoning [224]; AdaptiveStep [119]; Self-Calib [70];CISC [170]; ESC [92]; DSC [184]; PathC [247]; RPC [246]; Sleep-time Compute [104];SpecReason [138]; TOPS [214]; Retro-Search [124]; ThinkDeepFast"}],"limit":50,"offset":0}