Vikas Chandra

344 posts

Vikas Chandra

Vikas Chandra

@vikasc

Senior Director of #AI Research @Meta | CMU Ph.D. | Ex visiting faculty at Stanford

Menlo Park, CA Katılım Nisan 2009
180 Takip Edilen619 Takipçiler
Vikas Chandra
Vikas Chandra@vikasc·
Grateful to @sallywf and @EETimes for the thoughtful writeup of my Embedded Vision Summit keynote. The thesis in one line: the next decade of AI won't be won by the biggest model, but by the smartest, most efficient one that lives on the devices you wear! eetimes.com/scaling-down-i…
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Vikas Chandra
Vikas Chandra@vikasc·
Audio is the most ignored perception modality in on-device AI. Every smart glass, robot, and drone has a mic. Almost none fuse audio + vision at perception time. Vision-only is the vibe-coded version of multimodal perception.
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Vikas Chandra
Vikas Chandra@vikasc·
3/ Even Qwen2.5 Omni hits just 27.3% on foreground and 39.5% on background sound accuracy. Audio hallucination is widespread in today's AV-LLMs, and robust evaluation has to be a first-class metric for AR/wearable use cases. 📄 arxiv.org/pdf/2604.23860
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Vikas Chandra
Vikas Chandra@vikasc·
2/ Setup: 300 egocentric videos, 1,000 sound-focused Q/As, with a taxonomy that separates foreground action sounds (from the user's activity) and background ambient sounds.
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Vikas Chandra
Vikas Chandra@vikasc·
1/ New @ieeeICASSP 2026 (Oral): "Exploring Audio Hallucination in Egocentric Video Understanding." Audio-visual LLMs often "hear" things they didn't, inferring sounds from visual cues alone. We built a benchmark to quantify it.
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Vikas Chandra
Vikas Chandra@vikasc·
Long post pulling the field together, leaning on my group's work (EUPE, EfficientSAM, Efficient Track Anything, EdgeTAM, LongVU, EgoAVU, VideoAuto-R1, DepthLM, ParetoQ) placed against the parallel work in each section. v-chandra.github.io/efficient-vide…
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Vikas Chandra
Vikas Chandra@vikasc·
What's still hard is mostly deployment: streaming at hour-plus durations, sub-watt AR glasses, open-set anomaly detection, cross-camera reasoning, spatial grounding through cuts, closed-loop eval. The bottleneck moved from models to the stack around them.
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Vikas Chandra
Vikas Chandra@vikasc·
Efficient Video Intelligence in 2026 🧵 Five years ago video understanding meant action recognition on Kinetics-400. Now VLMs reason over hour-long footage, foundation-grade tracking runs at 16 FPS on a phone, and one sub-100M backbone replaces four specialized encoders.
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Vikas Chandra
Vikas Chandra@vikasc·
Diffusion models couldn't reason because RL was too expensive, not because the architecture was wrong. dTRPO collapses trajectory computation to one forward pass. On a 7B model: +9.6% GPQA, +4.3% HumanEval+. The architecture question is open again. Paper: arxiv.org/abs/2603.18806
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Vikas Chandra
Vikas Chandra@vikasc·
Standard approach to long video: more frames, bigger context. Tempo flips it. A small VLM reads the question first, then compresses the video around it. 6B params. 8K visual tokens. Outperforms GPT-4o and Gemini 1.5 Pro on hour-long videos. arxiv.org/pdf/2604.08120
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AVB
AVB@neural_avb·
Been thinking about what this paper really means. "Video diffusion" and "World Models" are becoming synonymous. Neural Computers are basically video diffusion world models for terminal envs and GUI. Lots of talk last week about automating Manim videos. In theory, we should be able to train these world models on a 10000 hours of diverse manim videos and "see where it goes" If a NC can generate outputs to terminal commands, it should be able to generate videos like this directly from prompt too. Without writing code.
AVB@neural_avb

x.com/i/article/2042…

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Vikas Chandra
Vikas Chandra@vikasc·
Classical computers run programs. Agents wrap models around programs. Neural Computers ask: what if the model is the program, the memory, and the machine? New paper exploring fully learned runtimes where computation emerges from weights alone. Paper: arxiv.org/abs/2604.06425
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Kelly Greer
Kelly Greer@kellyjgreer·
the market is reacting to the memory shortage by buying Sandisk assuming that labs continue to run less optimized versions of their own models and overspend on hardware but the longer term signal to take from the memory wall is to focus on the continued scaling down of models deployed compute has scaled 3x every 2 years while memory bandwidth has only scaled 1.6x over 20 years - every new GPU generation widens this gap - but we've also been grossly overusing memory via the common training mode. APOLLO showed that AdamW, the standard LLM optimizer, stores redundant state for every single parameter, and that coarser gradient approximations achieve the same training quality with a fraction of the memory, enabling model pre-training on a 1/8 the GPU capacity. many more proof points are coming to light re: optimizing algorithms and efficiency of smaller models. in fact small models can outperform much larger models by spending more compute at inference time good read on this from @vikasc
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Mingchen Zhuge
Mingchen Zhuge@MingchenZhuge·
🫱 Introducing 𝐍𝐞𝐮𝐫𝐚𝐥 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫s: 𝐰𝐡𝐚𝐭 𝐢𝐟 𝐀𝐈 𝐝𝐨𝐞𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐮𝐬𝐞 𝐜𝐨𝐦𝐩𝐮𝐭𝐞𝐫𝐬 𝐛𝐞𝐭𝐭𝐞𝐫, 𝐛𝐮𝐭 𝐛𝐞𝐠𝐢𝐧𝐬 𝐭𝐨 𝐛𝐞𝐜𝐨𝐦𝐞 𝐭𝐡𝐞 𝐫𝐮𝐧𝐧𝐢𝐧𝐠 𝐜𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐢𝐭𝐬𝐞𝐥𝐟? Beyond today's conventional computers, agents, and world models, Neural Computers (NCs) are new frontiers where computation, memory, and I/O move into a learned runtime state. We ask: whether parts of runtime can move inward into the learning system itself. This is our first step toward the Completely Neural Computer (CNC): a general-purpose neural computer with stable execution, explicit reprogramming, and durable capability reuse. Work done with Mingchen Zhuge (@MingchenZhuge), Changsheng Zhao, Haozhe Liu (@HaoZhe65347 ), Zijian Zhou (@ZijianZhou524 ), Shuming Liu (@shuming96 ), Wenyi Wang (@Wenyi_AI_Wang ), Ernie Chang (@erniecyc ), Gael Le Lan, Junjie Fei, Wenxuan Zhang, Zhipeng Cai (@cai_zhipeng ), Zechun Liu (@zechunliu ), Yunyang Xiong (@YoungXiong1 ), Yining Yang, Yuandong Tian (@tydsh ), Yangyang Shi, Vikas Chandra (@vikasc), Juergen Schmidhuber (@SchmidhuberAI)
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Vikas Chandra
Vikas Chandra@vikasc·
On-Device LLMs: State of the Union, 2026 Three years ago, running an LLM on a phone was a toy demo. Today, billion-parameter models run in real time. What changed? Not just faster chips - we had to rethink everything. Full post: v-chandra.github.io/on-device-llms/
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Vikas Chandra
Vikas Chandra@vikasc·
The AI industry is betting on hardware to solve the memory wall. But what if the bottleneck isn't hardware, but how we use it? New post on attacking memory constraints algorithmically—and why the "bigger is better" era is ending. v-chandra.github.io/ai-memory-wall/
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