A new corpus of 108 mixed string-numeric tables shows that advanced tabular learners with basic string embeddings perform well on most real-world data, while large LLM encoders help on free-text heavy tables.
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The llama 3 herd of models.arXiv e-prints, pages arXiv–2407
18 Pith papers cite this work. Polarity classification is still indexing.
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Aurora unifies speculative decoder training and serving via asynchronous RL on inference traces, delivering 1.5x day-0 speedup on frontier models and 1.25x adaptation gains on distribution shifts.
SynBench benchmarks DP text generators across nine datasets and uses a new MIA to show that public pre-training on portions of private data overestimates synthetic text quality and breaks DP privacy bounds.
Page-granular Flex-Mem and switchable Flex-NPU cut TrustZone LLM TTFT by ~10× vs a CMA strawman and ~2.4× vs a pipelined secure-NPU strawman on RK3588.
A distributional alignment metric d_NTP and a linear regression method LTV for task vectors that improves accuracy by 9.2% over baselines on classification and regression tasks across multiple LLMs.
Semantically invariant row and column permutations in tables can cause LLMs to output incorrect answers, and a gradient-based attack called ATP efficiently finds such permutations that degrade performance across many models.
A neuro-symbolic system using LLM-guided best-first search and Isabelle tools proves up to 77.6% of theorems on the seL4 benchmark, outperforming prior LLM methods and Sledgehammer.
veScale-FSDP uses RaggedShard and structure-aware planning to support block-wise quantization and non-element-wise optimizers while delivering 5-66% higher throughput and 16-30% lower memory than prior FSDP systems at massive scale.
GenAI framework generates isomorphic physics problem banks via prompt chaining and validates them with 17 language models that correlate with student performance (ρ up to 0.594), achieving homogeneous difficulty in 73% of deployed banks.
MVP-Nav reconstructs explicit 3D physical occupancy from monocular RGB using foundation models and integrates it with semantic priorities via a Multi-layer Value Map for grounded planning in zero-shot object navigation.
StockR1 unifies LLM-based financial reasoning and time-series forecasting by emitting verifiable forecast actions that condition a decoder, optimized via consistency-grounded RL to improve accuracy on QA and prediction tasks.
LAR learns a compact latent action space from trajectories that shortens the effective decision horizon for LLM agents, reducing token count and inference time while preserving task success.
RLVR exhibits implicit reward overfitting to training data and optimizes heavy-tailed singular spectra with rank-1 focus on reasoning capability.
Mean-plus-trace alignment of text embeddings into the image distribution lets MLLM pretraining run on unpaired text, and the resulting text-only recipe (ReVision) scores 49.75 versus 48.91 for a 1M paired-image baseline.
SPON adds a small set of trainable input-independent activation vectors as representational anchors, trained by distribution matching, to stabilize sparse activation in LLMs and recover performance lost to hidden-state distribution shifts.
Gradient analysis and ablations show DPO and PPO have different target directions and component roles in preference optimization for LLMs.
A 1.4B and a 2.6B looped (weight-tied, recurrent-depth) language model trained on 7.7T tokens match or exceed several 4B–8B transformer baselines on selected reasoning benchmarks.
LiSCP detects LLM-generated text via stylistic consistency profiling across paraphrased variants and reports up to 11.79% better cross-domain accuracy plus robustness to adversarial attacks.
citing papers explorer
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STRABLE: Benchmarking Tabular Machine Learning with Strings
A new corpus of 108 mixed string-numeric tables shows that advanced tabular learners with basic string embeddings perform well on most real-world data, while large LLM encoders help on free-text heavy tables.
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When RL Meets Adaptive Speculative Training: A Unified Training-Serving System
Aurora unifies speculative decoder training and serving via asynchronous RL on inference traces, delivering 1.5x day-0 speedup on frontier models and 1.25x adaptation gains on distribution shifts.
-
SynBench: A Benchmark for Differentially Private Text Generation
SynBench benchmarks DP text generators across nine datasets and uses a new MIA to show that public pre-training on portions of private data overestimates synthetic text quality and breaks DP privacy bounds.
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FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
Page-granular Flex-Mem and switchable Flex-NPU cut TrustZone LLM TTFT by ~10× vs a CMA strawman and ~2.4× vs a pipelined secure-NPU strawman on RK3588.
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Distributional Alignment as a Criterion for Designing Task Vectors in In-Context Learning
A distributional alignment metric d_NTP and a linear regression method LTV for task vectors that improves accuracy by 9.2% over baselines on classification and regression tasks across multiple LLMs.
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The Power of Order: Fooling LLMs with Adversarial Table Permutations
Semantically invariant row and column permutations in tables can cause LLMs to output incorrect answers, and a gradient-based attack called ATP efficiently finds such permutations that degrade performance across many models.
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Neuro-Symbolic Proof Generation for Scaling Systems Software Verification
A neuro-symbolic system using LLM-guided best-first search and Isabelle tools proves up to 77.6% of theorems on the seL4 benchmark, outperforming prior LLM methods and Sledgehammer.
-
veScale-FSDP: Flexible and High-Performance FSDP at Scale
veScale-FSDP uses RaggedShard and structure-aware planning to support block-wise quantization and non-element-wise optimizers while delivering 5-66% higher throughput and 16-30% lower memory than prior FSDP systems at massive scale.
-
Scalable Generation and Validation of Isomorphic Physics Problems with GenAI
GenAI framework generates isomorphic physics problem banks via prompt chaining and validates them with 17 language models that correlate with student performance (ρ up to 0.594), achieving homogeneous difficulty in 73% of deployed banks.
-
MVP-Nav: Multi-layer Value Map Planner Navigator
MVP-Nav reconstructs explicit 3D physical occupancy from monocular RGB using foundation models and integrates it with semantic priorities via a Multi-layer Value Map for grounded planning in zero-shot object navigation.
-
Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
StockR1 unifies LLM-based financial reasoning and time-series forecasting by emitting verifiable forecast actions that condition a decoder, optimized via consistency-grounded RL to improve accuracy on QA and prediction tasks.
-
Latent Action Reparameterization for Efficient Agent Inference
LAR learns a compact latent action space from trajectories that shortens the effective decision horizon for LLM agents, reducing token count and inference time while preserving task success.
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On the Implicit Reward Overfitting and the Low-rank Dynamics in RLVR
RLVR exhibits implicit reward overfitting to training data and optimizes heavy-tailed singular spectra with rank-1 focus on reasoning capability.
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Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models
Mean-plus-trace alignment of text embeddings into the image distribution lets MLLM pretraining run on unpaired text, and the resulting text-only recipe (ReVision) scores 49.75 versus 48.91 for a 1M paired-image baseline.
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Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity
SPON adds a small set of trainable input-independent activation vectors as representational anchors, trained by distribution matching, to stabilize sparse activation in LLMs and recover performance lost to hidden-state distribution shifts.
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What Is Preference Optimization Doing, and Why?
Gradient analysis and ablations show DPO and PPO have different target directions and component roles in preference optimization for LLMs.
-
Scaling Latent Reasoning via Looped Language Models
A 1.4B and a 2.6B looped (weight-tied, recurrent-depth) language model trained on 7.7T tokens match or exceed several 4B–8B transformer baselines on selected reasoning benchmarks.
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Lightweight Stylistic Consistency Profiling: Robust Detection of LLM-Generated Textual Content for Multimedia Moderation
LiSCP detects LLM-generated text via stylistic consistency profiling across paraphrased variants and reports up to 11.79% better cross-domain accuracy plus robustness to adversarial attacks.