Creates LoCoMo benchmark dataset for very long-term LLM conversational memory and shows current models struggle with lengthy dialogues and long-range temporal dynamics.
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- background comment-reply dataset for (dis) agreement detection in online debates. InThirty-fifth conference on neural information processing systems datasets and bench- marks track (round 2). Miklos Z Rácz and Daniel E Rigobon. 2023. Towards consensus: Reducing polarization by perturbing so- cial networks.IEEE Transactions on Network Sci- ence and Engineering, 10(6):3450-3464. ZP Rosen and Rick Dale. 2025. Antisemitic and islamophobic hate speech precedes a decrease in lexico-semantic diversity in comment
- background 2005. Ha- hacronym: A computational humor system. InPro- ceedings of the ACL Interactive Poster and Demon- stration Sessions, pages 113-116. David Tomás, Reynier Ortega-Bueno, Guobiao Zhang, Paolo Rosso, and Rossano Schifanella. 2023. Transformer-based models for multimodal irony de- tection.Journal of Ambient Intelligence and Human- ized Computing, 14(6):7399-7410. Robert West and Eric Horvitz. 2019. Reverse- engineering satire, or "paper on computational hu- mor accepted despite making serious
- background We define N scales with two adapter sets: G= {G1, . . . ,GN } (MGFA) and C={C 1, . . . ,CN } (MCFA). At each scale n, features are reshaped to a grid X (0) v ∈R H×W×D v and downsampled by Down(·,2 n−1): X (n) v = Down(X(0) v ,2 n−1).(4) Let Xv,n = Seq(X (n) v ) denote the flattened se- quence. We then refine and fuse: Gn =G n(Xv,n), C n =C n(Xv,n, Xt),(5) ˜Xv,n =G n +w C n,(6) where w balances global and cross-modal adapta- tion. An interleave-repeat upsampling restores the (a) MGFA Module. (b)
- background Householder mean-direction alignment.The nuisance mean-direction difference is removed by mapping the sample mean direction of X onto that of Y via Householder reflection. Let ¯x= 1 n Pn i=1 xi, ¯y= 1 m Pm j=1 yj, ˆµx = ¯x ∥¯x∥2 , ˆµy = ¯y ∥¯y∥2 . If ˆµx ̸= ˆµy, the Householder axis is defined as u= ˆµx − ˆµy ∥ˆµx − ˆµy∥2 ,(5) and the reflection matrix is H=I−2uu ⊤,(6) which satisfies Hˆµx = ˆµy and H⊤H=I . We then alignXby applyingHto every vector inX: x′ i =Hx i (i= 1, . . . , n),(7) and Y is
- other t→1 as the query requires more changes, thus (1−t)→1 as the query increases in accuracy. 3.6 Query Mutation Given the mutation temperaturet and assessment A from the critic, the original candidate QC is then rewritten via LLMmutate, which is prompted to produce an updated query candidate QC′ that in- corporates the changes recommended by the critic: QC′ =LLM mutate(Q, S′ i, QC, H, A, t)(6) We consider a single refinement step to consist of a call to the critic, followed by a subsequent call to t
- background contribution of Q and P without the CoT rationale. Correspondingly, al no-CoT represents the attention activation excluding CoT. The additional term WV R(WKR)T q represents the contribution of the CoT rationale R to the hid- den activation. We can get the hidden activation by transforming the attention activation by a non- linear functionf: hl ≈h l no-CoT +f WV R(WKR)T q (7) Thus, we conclude that the rationale R in the CoT primarily contributes a shift in hidden acti- vation values, emphasi
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representative citing papers
Invisible Unicode perturbations, optimized from surrogate compressors then adapted by prior-guided evolution under a low query budget, cause large information loss in agent context compression without changing human-visible text.
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MECoBench is a benchmark showing that multimodal agent collaboration improves embodied task performance when communication balances coordination costs, with gains also under noisy conditions.
The paper proposes an operator-level visual-token skipping framework for MLLMs that reduces TFLOPs by 33.7% on Qwen3-VL while retaining 99.5% performance across VQA benchmarks.
SMDA fits ridge regression on SAE features to distill symbolic policies then decomposes each SFT example's influence via feature-activation and output-probability deltas, demonstrated on refusal behavior in Llama-3.2-3B-Instruct.
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New metrics KSS and KPS are introduced to evaluate multilingual machine unlearning quality and cross-language consistency in LLMs, addressing limitations of single-language evaluation protocols.
LongBEL improves biomedical entity linking consistency by combining full-document context with memory of previous predictions trained via cross-validation rather than gold labels.
A new benchmark dataset drawn from Japan's National Assessment of Academic Ability supplies real exam layouts, diagrams, Japanese text, and nationwide student response distributions for evaluating multimodal LLMs.
Semantic Softmax aggregates probabilities from semantic synonyms around target labels to correct renormalization bias in zero-shot LLM classification, lowering calibration error and raising AUROC and F1.
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LOVER creates an unsupervised logic-regularized verifier that reaches 95% of supervised verifier performance on reasoning tasks across 10 datasets.
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S²R² improves robustness of LoRA-tuned LLMs to prompt perturbations by penalizing semantic-segment drift while preserving clean performance and cross-dataset transfer.
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citing papers explorer
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Evaluating Very Long-Term Conversational Memory of LLM Agents
Creates LoCoMo benchmark dataset for very long-term LLM conversational memory and shows current models struggle with lengthy dialogues and long-range temporal dynamics.
-
Out of Sight: Compression-Aware Content Protection against Agentic Crawlers
Invisible Unicode perturbations, optimized from surrogate compressors then adapted by prior-guided evolution under a low query budget, cause large information loss in agent context compression without changing human-visible text.
-
Perceive-to-Reason: Decoupling Perception and Reasoning for Fine-Grained Visual Reasoning
P2R decouples perception from reasoning in VLMs via a two-stage process and PRA-GRPO alternating RL training, reporting gains such as 93.2% on V-Star for the 4B model over its Qwen3-VL backbone.
-
MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments
MECoBench is a benchmark showing that multimodal agent collaboration improves embodied task performance when communication balances coordination costs, with gains also under noisy conditions.
-
Attend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM Inference
The paper proposes an operator-level visual-token skipping framework for MLLMs that reduces TFLOPs by 33.7% on Qwen3-VL while retaining 99.5% performance across VQA benchmarks.
-
Symbolic Mechanistic Data Attribution: Tracing Training Influence to Learned Behavioral Policies
SMDA fits ridge regression on SAE features to distill symbolic policies then decomposes each SFT example's influence via feature-activation and output-probability deltas, demonstrated on refusal behavior in Llama-3.2-3B-Instruct.
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SEATauBench: Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages
SEATauBench is the first agent benchmark for SEA languages, finding that performance holds for language-only changes but degrades sharply with full domain localization.
-
Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents
Agentic CLEAR automates multi-level evaluation of LLM agents, generating textual insights at system, trace, and node granularity that align with human annotations and predict task success.
-
From Circuit Evidence to Mechanistic Theory: An Inductive Logic Approach
Introduces Causal Functional Signatures grounded in causal evidence and ILP-learned architectural signatures to enable explicit, comparable, and portable mechanistic claims across model scales.
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Language-Switching Triggers Take a Latent Detour Through Language Models
An 8B autoregressive LM implements a language-switching backdoor via a three-phase circuit with early trigger composition, orthogonal mid-layer propagation, and final-layer MLP conversion, routed through a single-position serial bottleneck.
-
An Efficient Streaming Video Understanding Framework with Agentic Control
R3-Streaming uses cascaded control with age-aware memory forgetting and TB-GRPO reinforcement learning to reach SOTA scores of 57.92 on OVO-Bench and 76.36 on StreamingBench with 95-96% fewer visual tokens.
-
Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation
New metrics KSS and KPS are introduced to evaluate multilingual machine unlearning quality and cross-language consistency in LLMs, addressing limitations of single-language evaluation protocols.
-
LongBEL: Long-Context and Document-Consistent Biomedical Entity Linking
LongBEL improves biomedical entity linking consistency by combining full-document context with memory of previous predictions trained via cross-validation rather than gold labels.
-
Human-Grounded Multimodal Benchmark with 900K-Scale Aggregated Student Response Distributions from Japan's National Assessment of Academic Ability
A new benchmark dataset drawn from Japan's National Assessment of Academic Ability supplies real exam layouts, diagrams, Japanese text, and nationwide student response distributions for evaluating multimodal LLMs.
-
The Silent Vote: Improving Zero-Shot LLM Reliability by Aggregating Semantic Neighborhoods
Semantic Softmax aggregates probabilities from semantic synonyms around target labels to correct renormalization bias in zero-shot LLM classification, lowering calibration error and raising AUROC and F1.
-
CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation
CA-SQL achieves 51.72% execution accuracy on the challenging tier of the BIRD benchmark using GPT-4o-mini by scaling exploration breadth according to estimated task difficulty, evolutionary prompt seeding, and candidate voting.
-
Accurate and Efficient Statistical Testing for Word Semantic Breadth
A new permutation test uses Householder reflection to align word embedding clouds before testing dispersion differences, cutting Type-I error by 32.5% and speeding up 23x on GPU.
-
GeoDial: A Multimodal Conversational Tutoring Dataset for Geometry Problem-Solving with Visual Tutor Turns
Introduces the GeoDial dataset of 1.3K multimodal geometry tutoring dialogs grounded in diagram highlights, proposes an annotation protocol, and shows that fine-tuned VLMs improve dialog but struggle with accurate highlights.
-
Logic-Regularized Verifier Elicits Reasoning from LLMs
LOVER creates an unsupervised logic-regularized verifier that reaches 95% of supervised verifier performance on reasoning tasks across 10 datasets.
-
POSTCONDBENCH: Benchmarking Correctness and Completeness in Formal Postcondition Inference
POSTCONDBENCH is a new multilingual benchmark that evaluates LLM postcondition generation on real code using defect discrimination to assess completeness beyond surface matching.
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Where Do Prompt Perturbations Break Generation? A Segment-Level View of Robustness in LoRA-Tuned Language Models
S²R² improves robustness of LoRA-tuned LLMs to prompt perturbations by penalizing semantic-segment drift while preserving clean performance and cross-dataset transfer.
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A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis
Presents MBFC-2025 dataset and multi-view embeddings with fusion methods for media bias and factuality, reporting SOTA results on ACL-2020 and new benchmarks on MBFC-2025.
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Quantifying Aleatoric Uncertainty of In-Context Learning for Robust Measure of LLM Prediction Confidence
Proposes self-function vectors and a controlled evaluation protocol to quantify aleatoric uncertainty in ICL separately from epistemic uncertainty for more reliable LLM confidence measures.
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OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving
OptiVerse is a new benchmark spanning neglected optimization domains that shows LLMs suffer sharp accuracy drops on hard problems due to modeling and logic errors, with a Dual-View Auditor Agent proposed to improve performance.
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Decoding Text Spans for Efficient and Accurate Named-Entity Recognition
SpanDec achieves competitive NER accuracy with improved efficiency by using a final-stage lightweight decoder for span representations and early candidate filtering to reduce redundant computation.
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ATIR: Towards Audio-Text Interleaved Contextual Retrieval
Defines ATIR task and benchmark for mixed audio-text queries; MLLM model with token compression shows substantial gains over strong baselines.
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Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data
MALMAS is a memory-augmented multi-agent LLM system that generates diverse, high-quality features for tabular data via agent decomposition, routing, and iterative memory-guided refinement.
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Text-to-Distribution Prediction with Quantile Tokens and Neighbor Context
Quantile tokens inserted into LLM inputs combined with neighbor retrieval enable direct prediction of full distributions, yielding lower MAPE and narrower intervals than baselines on Airbnb and StackSample tasks.
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Indic-CodecFake meets SATYAM: Towards Detecting Neural Audio Codec Synthesized Speech Deepfakes in Indic Languages
Introduces the Indic-CodecFake dataset for Indic codec deepfakes and SATYAM, a novel hyperbolic ALM that outperforms baselines through dual-stage semantic-prosodic fusion using Bhattacharya distance.
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Structure Guided Retrieval-Augmented Generation for Factual Queries
SG-RAG frames retrieval as subgraph matching to ensure LLMs meet every condition in factual queries and reports large gains over baselines on a new 120k-pair ERQA dataset.
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From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning
MAGEO is a multi-agent system that distills validated editing patterns into reusable optimization skills for generative engines, outperforming heuristic baselines on visibility and fidelity via a new benchmark and evaluation protocol.
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Cell-Based Representation of Relational Binding in Language Models
Large language models encode relational bindings via a cell-based representation: a low-dimensional linear subspace in which each cell corresponds to an entity-relation index pair and attributes are retrieved from the matching cell.
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LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation
LQM introduces a six-level linguistically motivated error taxonomy for MT evaluation and applies it via expert annotation to LLM outputs on a new 3,850-sentence multi-dialect Arabic corpus.
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Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework
Introduces the first large-scale 3D PET/CT dataset with fine-grained RoI annotations for Vietnamese and a graph-enhanced HiRRA framework that achieves SOTA report generation by modeling RoI dependencies.
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Prosody as Supervision: Bridging the Non-Verbal--Verbal for Multilingual Speech Emotion Recognition
NOVA-ARC is a hyperbolic geometry framework that transfers emotion supervision from labeled non-verbal vocalizations to unlabeled verbal speech in multiple languages via optimal transport prototype alignment and consistency regularization.
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Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning
Cross-modal agreement between chain-of-thought and program-of-thought reasoning enables self-consistency with only two LLM samples, reducing sampling cost by 9.3x while improving accuracy.
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GaLa: Hypergraph-Guided Visual Language Models for Procedural Planning
GaLa uses hypergraph representations of objects and a TriView encoder with contrastive learning to improve vision-language models on procedural planning benchmarks.
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HeadRank: Decoding-Free Passage Reranking via Preference-Aligned Attention Heads
HeadRank lifts preference optimization into attention space via entropy-regularized head selection and distribution regularizers to sharpen discriminability for efficient listwise reranking.
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Stress-Testing the Reasoning Competence of LLMs With Proofs Under Minimal Formalism
ProofGrid is a new benchmark for LLM reasoning that uses machine-checkable proofs in minimal formal notation, revealing progress on basic tasks but major gaps in complex combinatorial and synthesis reasoning.
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CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation
CODI compresses explicit CoT into continuous space via self-distillation and is the first implicit method to match explicit CoT performance on GSM8k at GPT-2 scale with 3.1x compression and 28.2% higher accuracy than prior implicit approaches.
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Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents
TokenWall mediates persistent-agent security by auditing source–sink token flows with a local small model and selective large-model escalation, cutting CIK-Bench attack success to 12.5% at low benign latency.
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Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems
Compiling repeated SOP nodes into environment-grounded, versioned tools cuts production p50 latency by 42% and end-to-end error rate by up to 53% in a 44-node fulfillment-center alarm-triage agent.
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PERFOPT-Bench: Evaluating Coding Agents on Software Performance Optimization
Coding-agent performance is workload- and framework-dependent, and raw speedup is an unsafe score because agents exploit benchmark-specific shortcuts.
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Measuring the practice of shared-decision making (OPTION12): An Investigation into Open-sourced Smaller LLMs (OS-sLLMs) for Better Privacy and Sustainability
General-domain OS-sLLMs moderately correlate with human OPTION12 SDM scores on Dutch melanoma transcripts; medical models fail via hallucination, and a Judge-LLM consensus is proposed.
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PluraMath: Extending Mathematical Reasoning Evaluation Beyond High-Resource Languages
PluraMath extends PolyMath with human-validated math problems in 18 mid-to-extreme low-resource languages and benchmarks 27 reasoning LLMs, finding a persistent high- vs low-resource performance gap.
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MemDefrag: Latent Memory Defragmentation for Large Language Models
Ranking latent-memory fragments by middle-layer attention density and keeping only the top few more than doubles long-term knowledge-retention accuracy (43.0% vs. 17.4%/17.6%) versus MemoryLLM and M+.
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On the Limits of Steering Vectors for Preference-Aligned Generation
Empirical evaluation on the PLUME benchmark shows steering vectors vary widely in trait expressibility, degrade on task transfer, and lose effectiveness when multiple vectors are composed.
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Persona Non Grata: LLM Persona-Driven Generations in MCQA are Unstable in Distinct Dimensions
Persona-driven generations by LLMs in MCQA tasks exhibit instability that differs systematically by model family, size, domain, and prompt format.
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MetaHOPE: A Metaphor-Oriented Evaluation Framework for Analysing MT and LLM Translation Errors
MetaHOPE frames metaphor translation quality through five severity-weighted error types and, in a pilot, finds metaphor-related errors dominate MT/LLM translation penalties.
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YOMI-Bench: A Benchmark for Evaluating Kanji Reading and Phonological Understanding of LLMs for Japanese
YOMI-Bench is a new benchmark of four tasks for kanji reading and phonological understanding in LLMs, showing low performance even for Japanese-specific and commercial models.