“ORBITFLOW improves SLO attainment for TPOT and TBT by up to 66% and 48%, respectively, while reducing the 95th percentile latency by 38% and achieving up to 3.3x higher throughput compared to existing offloading methods.”

“The goal was to evaluate whether large language models can determine causal and logical consistency between a proposed character backstory and an entire novel (~100k words), rather than relying on local plausibility.”

“Results reveal varied performance across research domains, with high-performing workflows maintaining feasibility without sacrificing creativity.”

“Unsloth now enables 7x longer context lengths (up to 12x) for Reinforcement Learning!”

““Overall, our empirical observations strongly indicate that TTT-E2E should produce the same trend as full attention for scaling with training compute in large-budget production runs.””

“"Your AI, is it your strategist? Or just a search tool?"”

“It doesn't just retrieve chunks; it compresses relevant information into "Memory Tokens" in the latent space.”

“Evaluations on the Long Range Arena (LRA) benchmark demonstrate RMAAT's competitive accuracy and substantial improvements in computational and memory efficiency, indicating the potential of incorporating astrocyte-inspired dynamics into scalable sequence models.”

“"I was tired of the RAG implementation with ChatGPT, so I completely switched to Gemini Pro's 'brute-force long context'."”

“TTT-E2E keeps training while it reads, compressing context into its weights. The result: full-attention performance at 128K tokens, with constant inference cost.”

“The physical and digital architecture of the global "brain" officially hit a new gear.”

“RLMs successfully handle inputs up to two orders of magnitude beyond model context windows and, even for shorter prompts, dramatically outperform the quality of base LLMs and common long-context scaffolds.”

“PackKV achieves, on average, 153.2% higher memory reduction rate for the K cache and 179.6% for the V cache, while maintaining accuracy.”

“LoZA can achieve significant speed-ups both for prefill-intensive (e.g., retrieval-augmented generation) and decode-intensive (e.g., tool-integrated reasoning) cases.”

“HGMem extends the concept of memory beyond simple storage into a dynamic, expressive structure for complex reasoning and global understanding.”

“The Infini-attention model achieves up to 31% higher accuracy than the baseline at a 16,384-token context.”

“Trellis replaces the standard KV cache with a fixed-size memory and train a two-pass recurrent compression mechanism to store new keys and values into memory.”

“TTT-E2E scales with context length in the same way as Transformer with full attention, while others, such as Mamba 2 and Gated DeltaNet, do not. However, similar to RNNs, TTT-E2E has constant inference latency regardless of context length, making it 2.7 times faster than full attention for 128K context.”

“UniMAGE achieves state-of-the-art performance among open-source models, generating logically coherent video scripts and visually consistent keyframe images.”

“TabiBERT attains 77.58 on TabiBench, outperforming BERTurk by 1.62 points and establishing state-of-the-art on five of eight categories.”

“The article doesn't contain a direct quote, but the core idea is that "vLLM" and "PagedAttention" are optimizing the software architecture to overcome the physical limitations of VRAM.”

“The approach can reduce up to 80% of visual tokens while maintaining performance in long context settings.”

“vLLM V1 introduces the KV Connector architecture to solve this problem.”

“"QwenLong-L1.5: Post-Training Recipe for Long-Context Reasoning and Memory Management"”

“現在のLLM研究における最大の懸念は、コンテキストが長くなるほど初期の指示を失念し、論理が崩壊する「熱死（Contextual Entropy）」です。”

“The research aims for training-free and state-aware MLLM personalized assistants.”

“Temporal reasoning over long, multi-session dialogues is a critical capability for conversational agents.”

“The paper focuses on query-aware mixed-precision KV cache quantization.”

“The research focuses on long-context conditioning for anthropomorphic multi-speaker conversational synthesis.”

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“The research likely focuses on improving long context understanding within the RAG framework.”

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“Efficient Long-Context Distillation of Mathematical Reasoning from Multi-Mode Supervision”

“The article's context originates from ArXiv, indicating a peer-reviewed research paper.”

“The paper likely introduces or utilizes a training paradigm that focuses on optimizing model behavior during inference rather than solely during pre-training.”

“The article's core focus is on post-training methods.”

“CIP is a plug-and-play framework.”

“GPT-5.2 is our most advanced frontier model for everyday professional work, with state-of-the-art reasoning, long-context understanding, coding, and vision.”

“The paper focuses on training-free context-adaptive attention.”

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“ChipMind leverages Retrieval-Augmented Reasoning for circuit design.”

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“The paper likely details the architecture and performance of the AdmTree approach.”

“The paper focuses on egocentric video generation using long context diffusion.”

“The study focuses on evaluating long-context reasoning.”

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“The paper focuses on the failure of safety mechanisms.”

“The paper focuses on improving self-speculative decoding for long-context generation.”