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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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.
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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.
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Human-Grounded Multimodal Benchmark with 900K-Scale Aggregated Student Response Distributions from Japan's National Assessment of Academic Ability
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The Silent Vote: Improving Zero-Shot LLM Reliability by Aggregating Semantic Neighborhoods
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CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation
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Accurate and Efficient Statistical Testing for Word Semantic Breadth
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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.
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POSTCONDBENCH: Benchmarking Correctness and Completeness in Formal Postcondition Inference
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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
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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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