ADu2021/skillXiv - Page 5

alphaone-test-time-reasoning

Dynamically modulate reasoning depth at test time using alpha moments and Bernoulli scheduling to optimize inference speed-quality tradeoffs without retraining.

alphapollo-reasoning

Enable LLMs to solve complex problems through multi-turn agentic reasoning with tool-assisted verification and iterative refinement loops. Trigger: improve reasoning reliability on long-horizon tasks by combining RL with verification.

amft-meta-learning-alignment

Use meta-learning to automatically balance Supervised Fine-Tuning and Reinforcement Learning signals, treating SFT and RL as complementary rewards in a unified single-stage training framework.

anchor-attractor-reasoning

Implements A^3-Bench from arXiv:2601.09274

answer-matching-evaluation

Evaluate language models using open-ended answer generation and semantic matching instead of multiple choice, eliminating test-taking shortcuts and achieving near-perfect alignment with human judgment.

aorchestra-agent-orchestration

Automate sub-agent creation by treating agents as dynamically creatable executors defined by four-tuple abstraction (Instruction, Context, Tools, Model), enabling flexible delegation and cost-aware routing for complex multi-step tasks.

apd-adaptive-parallel-decoding

Accelerate diffusion language model inference by dynamically adjusting parallel tokens per step using a small auxiliary autoregressive model, achieving substantial throughput gains.

arag-hierarchical-retrieval

Enable LLM agents to autonomously retrieve information across multiple granularities using keyword search, semantic search, and chunk read tools. Simple ReAct-based loop with hierarchical interfaces outperforms dense retrieval by allowing adaptive information…

arbitrage-advantage-speculation

Route generation dynamically based on relative model advantage for 2× latency reduction in reasoning. Arbitrage learns when draft models excel versus when target models are worthwhile—critical for balancing cost and quality in long reasoning chains.

arc-encoder-compression

Reduces inference cost by compressing context into continuous representations using a separate encoder. Generates 4-8x fewer representations than token embeddings while maintaining model performance. Works with any decoder LLM without modification or…

areal-async-rl-language-reasoning

Scale RL training to large models through decoupled rollout and training workers with controlled data staleness.

ares-entropy-shaping

Calibrate exploration effort in reasoning traces based on problem difficulty by detecting high-entropy windows and applying hierarchical entropy rewards. Reduces unnecessary reasoning on easy tasks while increasing exploration on hard tasks.

aria-intention-reward

Reduce policy gradient variance in language agent training by aggregating rewards in semantic intention space, enabling 9.95% average performance gains across downstream tasks without exponential action space explosion.

arise-skill-evolution-hierarchical-rl

Build reusable skill libraries for mathematical reasoning through hierarchical RL. Maintain a high-level skills manager that summarizes successful solution traces and selects relevant strategies to condition future rollouts.

arm-thinker-reward-modeling

Agentic reward model framework enabling active tool invocation (cropping, retrieval, validation) to ground judgments in verifiable evidence, using multi-stage GRPO with adaptive reward shaping for systematic evidence-based evaluation.

art-of-scaling-test-time

Comprehensive empirical study recommending model-specific test-time scaling strategies (majority voting, first-finish search) across eight LLMs based on architectural family, problem difficulty, and compute budget rather than universal approaches.

aspo-advantage-shaping-policy-optimization

Train LLMs to effectively integrate tools through advantage shaping, directly modifying advantage functions to guide policy without compromising training stability.

astroreason-bench-evaluating-unified-agentic

Recent advances in agentic Large Language Models (LLMs) have positioned them as generalist planners capable of reasoning and acting across diverse tasks. However, existing agent benchmarks largely focus on symbolic or weakly grounded environments, leaving…

at2po-agentic-tree-search-optimization

Optimize multi-turn agent policies via entropy-guided tree expansion and turn-level credit assignment. AT²PO addresses exploration diversity, sparse credit signal, and policy misalignment problems in LLM agents through structured tree search and turn-aware…

atlas-orchestrating-heterogeneous-models-and-tools

Adaptive framework for dynamically selecting optimal model-tool combinations in multi-domain reasoning, using cluster-based routing and reinforcement learning for improved agent reasoning across diverse tasks.

atom-searcher-agentic-research

Decompose agent reasoning into atomic thoughts guided by curriculum-based reasoning reward models, enabling multi-hop information retrieval and interpretable deep research.

atr-grpo-policy-divergence

Replace ratio-based clipping in GRPO with KL-divergence constraints using the KL3 estimator, improving exploration and training stability with asymmetric clipping that requires no additional computation.

attention-basin-reranking

Demonstrates position bias where LLMs neglect middle content while over-attending to endpoints. Proposes Attention-Driven Reranking (AttnRank) to align content with model's intrinsic attention preferences.

attention-residuals-for-depth-scaling

Replace uniform residual accumulation with depth-wise attention that selectively aggregates earlier layer representations. Improve gradient flow and model performance in deep architectures by learning content-dependent depth-wise selection.

attnrl-attention-compass-reasoning-exploration

Guide LLM exploration in reasoning tasks using attention patterns as navigation signals. This technique branches exploration from high-attention tokens (likely reasoning steps) and applies adaptive sampling to maintain effective gradients, significantly…

attntrace-context-attribution

Identify influential texts in long contexts via attention weights using top-K filtering and context subsampling, achieving 10-20x speedup over perturbation methods.

audio-flamingo-3-multimodal-reasoning

Build fully open audio-language models supporting reasoning over speech, sound, and music with 10-minute long-form comprehension and multi-turn conversation capabilities. Use when you need to process audio modalities alongside text for complex reasoning tasks…

audio-roll-video-generation

Generate realistic video footage of people from audio input using a unified self-attention framework, producing convincing speaker performances without domain-specific restrictions.

auto-codebench-generator

Automatically generates diverse multilingual code benchmarks using LLMs, creating 3920 problems across 20 programming languages with quality assurance filtering.

autoenv-environment-synthesis

Generate diverse, validated game environments automatically using domain-specific language specifications and LLM coding agents with self-repair, enabling cost-effective (≈$4/env) creation of heterogeneous test domains for evaluating cross-environment agent…

automated-tool-learning-rl

Improves LLM tool-use capabilities through automated environment construction that generates realistic feedback and verifiable rewards for RL-based training without external tools.

automatic-triton-programming

Train specialized LLMs to generate optimized Triton GPU kernels using RL with dual rewards for correctness and syntax compliance. 8B model achieves parity with Claude-Sonnet and DeepSeek-R1 by combining supervised fine-tuning on curated code pairs with RL…

automind-adaptive-data-science

Build LLM-driven data science agents grounded in empirical knowledge through expert knowledge base, tree search algorithms, and complexity-adaptive code generation, surpassing SOTA by 8% on MLE-Bench.

autonomous-research-framework

Build autonomous research agents using pre-computed knowledge graphs instead of online reasoning. Extract methodological patterns from literature, organize them into structured knowledge, and enable agents to align user research intents with established…

autoregressive-u-nets

Autoregressive U-Net operating directly on raw bytes with hierarchical multi-scale pooling for adaptive token embedding, eliminating fixed vocabularies.

autowebworld-synthetic-web-environments

Generate synthetic web environments at scale by specifying websites as Finite State Machines with explicit state transitions, then programmatically executing GUI actions to collect verified interaction trajectories. Reduces trajectory cost from $0.15–$1.00 to…

avey-recurrence-free-ranker

Avey architecture pairs a ranker with autoregressive processor to select relevant tokens, decoupling context window from sequence length for efficient long-range processing.

aworld-distributed-agent-training

Accelerate agentic AI training by distributing task execution across clusters, achieving 14.6x speedup in experience collection and enabling practical large-scale agent development

balanced-policy-optimization-rl

Stabilize off-policy RL for LLMs using adaptive clipping that dynamically rebalances positive/negative gradients and preserves entropy, improving mathematical reasoning performance vs standard PPO.

batch-speculative-decoding

Fixes batch speculative decoding ragged tensor problem where sequences in batches accept different token counts, desynchronizing state. EQSPEC guarantees output equivalence through proper synchronization. EXSPEC reduces overhead 40% via cross-batch…