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#Papers

Papers are scientific research articles published by labs or universities to present new findings in AI. For example, the paper "Attention Is All You Need" (Google, 2017) introduced the Transformer architecture.

40Articles
5Sources
74Avg. signal
Reddit r/MachineLearning·

Backpropagation destroys V1 brain alignment in one epoch, tracking RSA alignment to fMRI across training for BP, FA, predictive coding, and STDP [R]

Comparative study of learning rules (backprop, feedback alignment, predictive coding, STDP) via RSA alignment with human V1 fMRI. Backprop destroys 90% of V1 alignment after 1 epoch (r: 0.102→0.011), while PC and STDP lose only 25-31%. At epoch 40: PC/STDP >> BP/FA. Suggests fundamental trade-off between global error signals (higher layers) and early-layer alignment.

AlignmentBenchmarksPapers
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arXiv cs.LG·

Quantized Reasoning Models Think They Need to Think Longer, but They Do Not

Post-training quantization (PTQ) reduces reasoning model accuracy and increases chain-of-thought length. 52% of failures involve correct intermediate answers not output as final answers. A training-free logit penalty on overthinking markers ("wait", "but", "alternatively") reduces CoT length by 12-23% while preserving accuracy across 5 models (1.5B-32B) and 5 benchmarks.

ReasoningFine-tuningBenchmarks
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arXiv cs.CL·

Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval

DOPA, a demonstration retrieval framework, uses an OOD proxy to approximate the inaccessible target domain and guide selection of relevant demonstrations. A Mahalanobis distance-based global diversity constraint ensures sufficient variety among retrieved examples. Positive results across multiple LLMs and tasks under severe distribution shift.

Prompt engineeringBenchmarksPapers
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72
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arXiv cs.CL·

SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering

SPADER is an RL framework for tool-augmented LLM agents in Multi-Answer QA. It introduces Step-wise Peer Advantage (SPA) for fine-grained credit assignment over long trajectories, and a diversity-aware exploration reward promoting rare entity discovery. Evaluated on QAMPARI, Mintaka, WebQSP, QUEST: improves recall and F1 vs prompting and supervised RL baselines.

AI AgentsReinforcement learningReasoning
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78
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arXiv cs.AI·

TIGER: Traceable Inference with Graph-Based Evidence Routing for Mitigating Hallucinations in Multimodal Generation

TIGER is an inference-time framework to mitigate hallucinations in multimodal generation. It independently extracts an observation graph from input and a claim graph from output, then assigns risk scores to claims based on support and conflict. The model repairs high-risk claims while keeping the backbone frozen. Convergence analysis shows geometric risk reduction to an explicit asymptotic bound.

ReasoningVisionPapers
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78
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arXiv cs.CL·

Parameter Alignment Mitigates Catastrophic Forgetting in Multilingual Expert Language Models

Study on preventing catastrophic forgetting during continual pretraining of multilingual language models. Authors propose five parameter alignment strategies (layer freezing, regularization, post-hoc reversion, model merging) tested across 32 languages and four evaluation axes. Parameter alignment substantially reduces forgetting while maintaining language acquisition.

Fine-tuningPapersBenchmarks
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78
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arXiv cs.AI·

Threshold-Based Exclusive Batching for LLM Inference

arXiv paper on LLM inference batching optimization. Authors demonstrate mixed batching (MB) is suboptimal on bandwidth-constrained GPUs: exclusive batching (EB) achieves 41.9% higher throughput on RTX PRO 6000 (1.792 TB/s). They propose EB+, a hybrid scheduler that dynamically switches between EB and MB based on GPU bandwidth, model size, and workload composition, reaching 36.4% gains under non-stationary traffic.

InfrastructureBenchmarksPapers
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78
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arXiv cs.LG·

RAFT: Data Refinement and Adaptive Distillation for Domain Fine-Tuning with Alleviated Forgetting

RAFT is a two-stage domain fine-tuning method that mitigates catastrophic forgetting. It refines data via self-conditioned rewriting and answer fusion, then applies on-policy distillation where the original model provides soft targets on student-generated trajectories. Across five domains, RAFT improves domain accuracy by 23.2% over standard SFT and recovers 18.2% of degradation on MS-Bench.

Fine-tuningReinforcement learningPapers
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78
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arXiv cs.LG·

Beyond Augmentation: Score-Guided Pathological Prior for EEG-based Depression Detection

Novel approach for Major Depressive Disorder detection from EEG without data augmentation. SGC (Score-Guided Classification) uses an unsupervised generative network to model pathological anomalies as prior, fused with deep feature representations. Cross-Channel Spatial Adaptation module handles multi-center channel heterogeneity. Validated on Mumtaz2016 and MODMA datasets.

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