AIBullisharXiv – CS AI · Jun 57/10
🧠Researchers propose CKA-QAD, a new method for quantizing large language models to NVFP4 precision that preserves internal representational geometry rather than just matching output distributions. The approach addresses a critical limitation in existing quantization-aware distillation techniques, showing significant improvements in reasoning and coding task performance across multiple model architectures.
AIBullisharXiv – CS AI · Jun 57/10
🧠Researchers introduce Dynamic Thinking-Token Selection (DynTS), a method that optimizes Large Reasoning Models by identifying and retaining only decision-critical tokens during inference while discarding redundant reasoning trace data. This approach significantly reduces memory footprint and computational overhead, addressing a major efficiency bottleneck in LRMs that generate extended reasoning sequences.
AIBullisharXiv – CS AI · Jun 57/10
🧠Researchers introduce Active Video Perception (AVP), an AI framework that enables agents to actively seek relevant evidence in long videos rather than passively processing entire content. The system uses an iterative plan-observe-reflect process to achieve superior accuracy on five benchmarks while reducing inference time by 82% and token usage by 88% compared to existing agentic methods.
AIBullisharXiv – CS AI · Jun 57/10
🧠Researchers introduce AdaMEM, a test-time adaptive memory framework that enables language agents to dynamically adjust behavior during inference without updating model parameters. The system combines persistent offline trajectory memory with dynamically generated on-the-fly strategy memory, demonstrating 11-13% performance improvements on complex reasoning and web interaction tasks.
AIBullisharXiv – CS AI · Jun 57/10
🧠Researchers introduce ReTreVal, a training-free framework that enables large language models to learn from failures across multiple problems without fine-tuning. By implementing adaptive tree exploration, typed-failure backtracking, and cross-problem memory, ReTreVal achieves significant performance improvements on mathematical and knowledge reasoning tasks, allowing a 32B model to match much larger systems.
AIBullisharXiv – CS AI · Jun 47/10
🧠Researchers present Recover-LoRA, a technique that recovers accuracy in large language models aggressively quantized to 2-bit precision by applying low-rank adapters trained on synthetic data. The method achieves 7.5-23.3% throughput improvements while recovering 80-95% of lost accuracy on most benchmarks, enabling practical deployment of compressed models on edge devices.
AIBullisharXiv – CS AI · Jun 47/10
🧠MIRAGE is a new AI framework that enables mobile agents to reason internally using compressed latent representations instead of generating verbose reasoning chains. By aligning hidden states with future interface screenshots, the system achieves comparable performance to explicit chain-of-thought approaches while reducing token generation by 3-5x, offering significant efficiency gains for AI-powered mobile automation.
AIBearisharXiv – CS AI · Jun 47/10
🧠Researchers demonstrate the first practical quantization-conditioned attack that reliably compromises large language models across advanced quantization methods including AWQ, GPTQ, and GGUF. The attack exploits how outlier weights cause rounding errors in modern quantization schemes, allowing adversaries to inject hidden malicious behaviors that activate only after quantization, posing significant security risks to the deployment pipeline.
AIBullisharXiv – CS AI · Jun 47/10
🧠Researchers introduce Speculative Thinking, a training-free framework that leverages larger AI models to guide smaller ones during inference, improving reasoning accuracy while reducing output length. The method achieves a 6.2% accuracy boost on mathematical reasoning tasks for a 1.5B parameter model with 15.7% shorter outputs, demonstrating efficiency gains without costly retraining.
AIBullisharXiv – CS AI · Jun 27/10
🧠Xiaomi researchers have developed MiCU, a domain-specific large language model optimized for smart home command understanding that handles ambiguous user requests better than traditional systems. The model employs curriculum learning, reinforcement learning, and token compression techniques, achieving 20% average accuracy gains and reducing user correction rates by 1.57% in production deployment across 1.7 million daily active users in the Xiaomi Home app.
AIBullisharXiv – CS AI · Jun 27/10
🧠TIGER is a new inference-time framework designed to reduce hallucinations in multimodal AI models by extracting observation graphs from inputs and claim graphs from outputs, then scoring and repairing unsupported claims. The method demonstrates improvements across image-to-text, audio-to-text, and video-to-text generation tasks while maintaining output quality and keeping the model backbone frozen.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers introduce STARFISH, a novel neural network healing method that efficiently recovers accuracy lost during weight pruning by aligning pruned networks with original internal state representations using minimal unlabeled calibration data. The technique achieves up to 22% accuracy improvement over existing methods and recovers 82% of original performance after removing 75% of weights from vision transformers.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers introduce EPIC, an efficient decoding framework for diffusion language models that operate under context-free grammar constraints. The method reduces inference time by up to 67.5% compared to existing CFG-constrained approaches while preserving the parallel decoding advantage that makes diffusion models competitive with autoregressive alternatives.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers demonstrate that 2-bit quantization of large reasoning models causes instability leading to longer inference traces rather than speedup, but introduce lightweight recovery techniques (FP16 planning and loop rescue) that restore accuracy from 17-65% to 74-87% while maintaining computational efficiency.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers propose the Intelligent Computing Architecture Model (ICAM), a six-layer framework that applies classical computer architecture principles to large language models and agentic AI systems. The paper maps recurring engineering challenges—cache reuse, context management, agent scheduling, and permission control—to traditional systems problems, introducing three design laws to optimize model-native computing efficiency and coordination.
🧠 Claude
AIBullisharXiv – CS AI · Jun 27/10
🧠Grokers introduces an architecture that shifts AI comprehension costs from query time to write time by using autonomous agents to pre-analyze and enrich typed knowledge graphs, eliminating repeated language model calls through inductive dependency traversal. The system proves three formal theorems about cache efficiency, interaction resolution, and correct traversal ordering while providing a deterministic alternative to embedding-based search.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers demonstrate that latent reasoning in transformer models functions as a policy improvement operator rather than simply adding computational depth. By applying reinforcement learning and diffusion training methods, they achieve 18x reduction in forward passes while maintaining performance, revealing how recursive steps either contribute meaningfully or become dead compute.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers introduce ACON, a framework that compresses long-context information for LLM agents without model fine-tuning, reducing token usage by 26-54% while improving task success rates. The method optimizes compression through natural language refinement and enables smaller language models to function effectively as long-horizon agents.
AIBullisharXiv – CS AI · Jun 27/10
🧠BitsMoE introduces a spectral-energy-guided quantization framework for compressing Mixture-of-Experts large language models, achieving significant improvements in the ultra-low-bit regime. The method uses SVD decomposition to intelligently allocate bits across expert weights, delivering 27.83 percentage point accuracy improvements over existing approaches at 2-bit quantization while accelerating inference speed by 1.76× on Qwen models.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers have developed IDLM (Inverse-distilled Diffusion Language Models), a technique that accelerates text generation in diffusion language models by reducing inference steps by 4x-64x while maintaining output quality. The method adapts inverse distillation—previously used for continuous diffusion models—to discrete language settings, addressing theoretical uniqueness challenges and practical gradient stability issues through novel mathematical formulations.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers introduce LatentMAS, a framework enabling LLM agents to collaborate directly in latent space rather than through text, achieving up to 14.6% higher accuracy while reducing token usage by 70.8%-83.7% and improving inference speed 4× faster than text-based multi-agent systems.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers introduce TAPS, a target-aware prefix selection method that improves speculative decoding by optimizing how draft trees are verified in diffusion models. The technique achieves up to 7.9x speedup over standard autoregressive decoding and outperforms competing methods by 1.36-1.74x, addressing a fundamental inefficiency where existing approaches verify unreachable token sequences.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers introduce stochastic backtracking, a novel test-time scaling method for language models that revisits previously generated solution paths rather than committing irreversibly to frontier candidates. The approach uses subpool selection and power backtrack sequential Monte Carlo to improve reasoning accuracy while reducing token generation, outperforming existing PRM-guided methods across mathematical benchmarks.
AIBullisharXiv – CS AI · Jun 27/10
🧠FastSLM introduces a Hierarchical Temporal Abstractor (HTA) that compresses long-form speech into just 1.67 tokens per second—a 97% reduction—while maintaining competitive performance on speech understanding benchmarks. This architecture solves a critical scaling bottleneck for multimodal AI models by preserving acoustic detail despite extreme compression, enabling efficient deployment of speech-capable language models.
AIBullisharXiv – CS AI · Jun 27/10
🧠Researchers propose T1, a tool-integrated verification framework that enables small language models to effectively verify outputs during test-time compute scaling by offloading memorization-heavy tasks to external tools. The approach demonstrates that a 1B parameter model can outperform an 8B model on mathematical benchmarks when equipped with tool integration, addressing a critical limitation in deploying smaller models at inference time.
🧠 Llama
