CrossPool separates weights and KV-cache into distinct GPU pools plus a planner, virtualizer, and layer-wise scheduler to cut P99 time-between-tokens by up to 10.4x versus prior kvcached multi-LLM systems.
Mixed citations
InProceedings of the 2023 Conference on Em- pirical Methods in Natural Language Processing, Houda Bouamor, Juan Pino, and Kalika Bali (Eds.)
Mixed citation behavior. Most common role is background (60%).
31
Pith papers citing it
Background
60% of classified citations
citation-role summary
background 3
method 2
citation-polarity summary
representative citing papers
QCFuse achieves full-prefill quality in RAG with 1.7x average prefill speedup over full prefill and 1.5x over ProphetKV via compressed query-aware cache fusion.
MemPoison enables stealthy memory poisoning in LLM agents via dialogue by using semantic relational bridges, entity masquerading, and joint embedding optimization to bypass selective extraction and rewriting, achieving up to 0.95 attack success rate.
SilentRetrieval is a data poisoning attack achieving 84.6% HR@10 and 57.5% ASR-LLM on Natural Questions via coordinated beam search and trigger fusion while preserving document fluency.
Layer-wise Token Compression applies adaptive token pooling at middle transformer layers for cross-encoder rerankers, preserving MS MARCO ranking quality while raising QPS up to 25% on passages and 116% on documents, with added gains on listwise LLM rerankers and a regularizer effect for long inputs
DiffCodeGen clusters code candidates by behavioral similarity from fuzzing-synthesized inputs and selects the largest cluster's medoid, matching or exceeding prior test-time scaling methods with far less token and time cost.
IPQA is a new benchmark that measures how well models identify core user intents from history in personalized question answering, finding that performance is poor and declines with greater question complexity.
Crowdsourced metaphors show rising anthropomorphism and warmth toward AI that predict trust and adoption, with notable demographic differences.
ActPlane introduces an OS-kernel policy engine using an information-flow control DSL and eBPF to enforce agent harness policies, achieving better compliance on indirect paths with 1.9-8.4% overhead.
A multi-LLM council scores predictive processing papers on an expert ontology, maps results in 3D hypothesis space, and introduces a dispersion metric showing greater spread in global versus local oddball paradigms.
SPLADE models produce wacky expansion terms whose prevalence rises with larger vocabularies and falls with stricter sparsity; these terms primarily aid in-domain retrieval rather than out-of-domain generalization.
Introduces bounded relational presence as a designable, tunable, and withdrawable quality for conversational AI that supports engagement while avoiding claims of personhood or human equivalence.
Symptom Induction compresses labeled data into interpretable guidelines that improve LLM classification of depression symptoms in text, outperforming zero-shot, in-context, and fine-tuning approaches with gains on rare symptoms and cross-disease generalization.
GRASP uses grounded chain-of-thought with a coordinate-weighted SFT stage and a GRPO-style policy-optimization stage to identify sarcasm targets in text and images, evaluated on the authors' new MSTI-MAX dataset.
An Explicit Logic Channel of LLM, VFM and probabilistic inference validates and improves zero-shot MLLMs via Consistency Rate without ground-truth labels.
LLM-driven index tuning can outperform Microsoft's Database Tuning Advisor on some real-world queries but is too variable to deploy without expensive validation.
MirrorBench defines a reproducible benchmark combining lexical metrics (MATTR, Yule's K, HD-D) and LLM-judge metrics with calibration controls to measure human-likeness of user-proxy agents across four datasets.
MM-Telco creates multimodal benchmarks for telecom and demonstrates that fine-tuned LLMs and VLMs achieve significant performance gains on domain-specific tasks.
NeuS-E is a post-generation refinement method that uses neuro-symbolic analysis of a formal video representation to detect and correct semantic and temporal inconsistencies in text-to-video outputs, improving prompt alignment by nearly 40%.
Akashic’s MemAttention plus locality-aware placement improves agent task accuracy by up to 10.2 points and throughput by up to 1.21× over prior memory systems across four long-horizon workloads.
CoCoMUT is a reusable pipeline that discovers project structure, constructs call graphs, extracts source, reconciles bytecode to source, and emits versioned JSON datasets of method contexts, demonstrated on 20 Java repositories with 97.8% reconciliation and 99% audit accuracy.
A dual-agent closed-loop system integrates Theory of Mind reasoning with multimodal video generation to create social avatars that outperform full-information baselines on dialogue quality under information asymmetry.
EgoCoT-Bench provides 3,172 verifiable QA pairs across perception, anticipation, and reasoning tasks on egocentric videos, revealing that many MLLMs give answer-correct but evidence-inconsistent explanations.
TextClusterLab introduces an LLM-driven generator for synthetic text clustering datasets with tunable attributes and a suitability benchmark for evaluation.
citing papers explorer
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GRASP: Grounded CoT Reasoning with Dual-Stage Optimization for Multimodal Sarcasm Target Identification
GRASP uses grounded chain-of-thought with a coordinate-weighted SFT stage and a GRPO-style policy-optimization stage to identify sarcasm targets in text and images, evaluated on the authors' new MSTI-MAX dataset.
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ChipLingo: A Systematic Training Framework for Large Language Models in EDA
ChipLingo trains LLMs on EDA data via corpus construction, domain-adaptive pretraining, and RAG scenario alignment, reaching 59.7% accuracy with an 8B model and 70.02% with a 32B model on a new internal EDA benchmark.