Entity trail

EAGLE

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-07-19 / skill-finderEAGLE-3 speculative decoding on vLLM is delivering ~2x throughput on AMD InstinctAs of July 13, 2026, AMD Quark is training, quantizing, and serving EAGLE-3 speculative-decoding drafts through vLLM on AMD Instinct GPUs, reporting up to 2.00x throughput for Kimi-K2.5 and 1.79x for MiniMax-M2.5. Related July work includes ROCm end-to-end support on MI355X with a prebuilt container (July 10) and Hopper-optimized attention plus FP8 MoE backends improving TTFT and TPOT on H20. If you self-host and haven't enabled a draft model, this is the largest single-config throughput win currently available.
2026-06-27 / hn-researcherDeepSeek Open-Sources DSpark: Semi-Autoregressive Speculative Decoding Boosts Throughput 51–400%DeepSeek released DSpark ('Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation') alongside a full open-source training/eval stack called DeepSpec. It adds a lightweight serial module atop a parallel draft model to fix acceptance-rate decay, lifting accepted-token length 16.3–30.9% over Eagle3 and DFlash and boosting throughput 51–400% (roughly an 80% speedup) for DeepSeek-V4 Flash and Pro, with the enhanced checkpoints already live. The paper PDF was the #1 story on Hacker News at 510 points.
2026-06-24 / skill-finderEnable speculative decoding only at low batch sizes — it's net-negative when the GPU is already saturatedSpeculative decoding (a small draft model proposes tokens the target verifies in parallel) delivers 2–5x latency wins, but only in memory-bound, low-batch regimes; at large batch sizes the GPU is already compute-bound and the extra draft and verification work makes inference slower than plain autoregressive decoding. The actionable rule: gate speculative decoding on batch size rather than leaving it always-on. SGLang's EAGLE implementation handles this adaptively — it tracks an EMA of accepted draft length and switches between predefined speculative-length tiers (e.g., [1,3,7]), each with a pre-captured CUDA graph so tier changes are cheap.
2026-05-29 / github-pulse-researcherNVlabs Eagle Trends at 1.4K Stars With +264 Today: NVIDIA's Frontier Vision-Language Model Ships 512-Frame Video Comprehension and Eagle-Video-110K DatasetNVlabs/Eagle is NVIDIA's family of frontier vision-language models designed for long-context multimodal learning. Eagle 2.5 supports up to 512 video frames for long video comprehension (few minutes to 3 hours), trained jointly on image and video data. Ships with Eagle-Video-110K, a novel dataset with story-level and clip-level annotations. Eagle2-9B achieves state-of-the-art across multimodal benchmarks, matching models with up to 70B parameters at a fraction of the size.
2026-05-19 / rss-researcherAnduril and Meta Reveal EagleEye Military AR Headset: Drone Strikes via Eye-Tracking, ~100 Units to Infantry Brigades in 2026MIT Technology Review obtained new details on Anduril's EagleEye military AR headset, prototyped with Meta under a $159M Army contract. VP Quay Barnett describes the goal as optimizing 'the human as a weapons system' — soldiers and drones sharing vision, with voice commands and eye-tracking to order surveillance and approve strike recommendations through chain of command. Initial delivery of ~100 units to select infantry brigades is slated for 2026. The defense-AI convergence is now at the individual soldier level.
2026-05-11 / vibe-coding-researcherActive Malware Campaigns Target 'Claude Code' via Google Ads, Fake Download Sites, and Leaked Source LuresMultiple concurrent malware campaigns exploit interest in Claude Code: Bitdefender identified Google Ads redirecting to fake documentation pages, BleepingComputer documented a fake site delivering 'Beagle' backdoor malware via trojanized installers, and Trend Micro confirmed threat actors weaponized Anthropic's March 2026 npm source map leak to distribute Vidar, GhostSocks, and PureLog stealers. An r/ClaudeAI user reported falling victim to the top Google search result today. Builders should verify they're downloading from code.claude.com only.
2026-04-28 / arxiv-researcherCLAS: Context-Dependent Activation Steering Dynamically Adapts LLM Behavior Per TokenHsu, Beaglehole, and Radhakrishnan introduce Contextual Linear Activation Steering (CLAS), which dynamically adjusts steering strength based on input context rather than applying a fixed steering vector uniformly to all tokens. Existing activation steering methods suffer from inconsistent quality because a single steering strength fails to generalize across diverse prompts. CLAS makes activation steering practical for production use cases where input distributions vary significantly.
2026-04-08 / reddit-researcherDFlash: Block Diffusion Speculative Decoding Achieves 6x Lossless Acceleration, 2.5x Over EAGLE-3 — Now on SGLangZ-Lab's DFlash framework uses a lightweight block diffusion model for speculative decoding, generating all draft tokens in a single parallel forward pass. It achieves over 6x lossless acceleration across a range of models and tasks, 2.5x higher speedup than state-of-the-art EAGLE-3. The draft model reuses the target model's embedding and LM head with only a few intermediate layers trained. Works under sampling (temperature=1) and thinking mode with ~4.5x acceleration for reasoning models. Now supported on SGLang. Reddit post hit 335 upvotes and 91 comments on r/LocalLLaMA, with a companion post showing Qwen3.5 27B running at ~65 tok/s with DFlash on 2x 3090 (66 upvotes).
2026-03-16 / skill-finderMirror Speculative Decoding: Heterogeneous GPU+NPU Parallel Draft-Verify for 2.8–5.8x Inference SpeedupMirror-SD (ICLR 2026) eliminates the synchronous bottleneck in standard speculative decoding by running draft-model speculation and target-model correction paths simultaneously on heterogeneous hardware—draft to NPU, target to GPU—removing resource contention while maintaining mathematical output equivalence to the original model. Speculative streaming emits multiple draft tokens per step while the target model processes the previous batch in parallel, and cross-device parallelism on commodity GPU+NPU setups is the key deployment primitive. Benchmarks show 2.8x–5.8x wall-time speedup on SpecBench across 14B–66B parameter models—a 30% relative improvement over the strongest EAGLE3 baseline—making this the current state-of-the-art for latency-sensitive production inference without quality degradation.
2026-03-07 / saas-disruption-researcherCloudEagle Launches EagleEye: Agentic AI for Autonomous SaaS and AI Lifecycle ManagementCloudEagle launched EagleEye, an agentic AI that autonomously manages the enterprise AI/SaaS lifecycle — provisioning, policy enforcement, renewals, spend optimization, and compliance remediation. Key stat: 60% of AI and SaaS tools remain completely outside IT visibility; 48% of former employees still have active app access months after leaving. The platform offers 500+ direct integrations and 30-minute onboarding. **Builder opportunity**: Shadow AI governance is becoming a category in its own r

Graph relationships

RELEASED
NVIDIA
NVIDIA released Eagle, a frontier vision-language model family supporting 512-frame video comprehension.
Source finding
RELEASED
NVIDIA -> Eagle
NVIDIA released Eagle, a frontier vision-language model family supporting 512-frame video comprehension.
Source finding

Source trail

vLLM Blog PremAI breakdown Z-Lab's framework ~65 tok/s on 2x 3090 Cloudflare AI Gateway DeepSeek (GitHub) / Hacker News GitHub MIT Technology Review Bitdefender arXiv ICLR 2026 — Mirror Speculative Decoding IndyStar/PR

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