Vrinda Kohli

Vrinda Kohli

Beyond the Benchmark: Why TruthTensor Might Be the Eval Framework We've Been Missing

Beyond the Benchmark: Why TruthTensor Might Be the Eval Framework We've Been Missing

When was the last time you confidently trusted a benchmark to tell you how an LLM would actually perform in production? The gap between benchmark performance and real-world reliability is significant, and it's a problem that deserves more attention. I recently read through this paper by Inference Labs,

Making Voice Assistants Faster Without Losing Accuracy

Making Voice Assistants Faster Without Losing Accuracy

Have you ever noticed how some voice assistants seem to understand you instantly, while others leave you waiting? That delay isn't random. It's the result of a fundamental trade-off that has plagued speech recognition for years. Systems could either be fast or accurate, but rarely both.

Semantic Highlighting: Making RAG Cheaper Without Compromises

Semantic Highlighting: Making RAG Cheaper Without Compromises

Recent research from the Zilliz team tackles a problem that shows up constantly in production RAG systems: how do you actually show users why a document is relevant to their query? Consider a typical scenario. A user asks: "How can I speed up my Python code?" The vector

Breaking the Context Window: How Recursive Language Models Handle Infinite Input

Breaking the Context Window: How Recursive Language Models Handle Infinite Input

Long-context understanding has been a persistent challenge in language model research. Despite architectural innovations (ALiBi, YaRN, RoPE variants) and massive context window expansions (Claude 3.5 at 200k tokens, GPT-5 at 256k+), models still exhibit performance degradation on long inputs, a phenomenon known as "context rot." The community

Streaming Speech Synthesis Without the Trade-offs: Meet StreamFlow

Streaming Speech Synthesis Without the Trade-offs: Meet StreamFlow

The last few years of neural speech synthesis have been wild. Flow matching models, diffusion transformers, and insanely natural TTS systems keep raising the bar. The catch? Most of these models expect to see the whole audio sequence at once . While this is great for offline generation, but if you’

VITA-Audio: Making AI Voice Assistants Actually Feel Instant

VITA-Audio: Making AI Voice Assistants Actually Feel Instant

Have you ever noticed that frustrating pause when you ask your voice assistant a question? You speak, there's a beat of silence, and then it finally starts responding. That delay might seem minor, but it breaks the natural flow of conversation and reminds you that you're

Kimi K2 Thinking: Engineering Deep Reasoning at Scale

Kimi K2 Thinking: Engineering Deep Reasoning at Scale

Introduction Moonshot AI recently open-sourced Kimi K2 and its reasoning-optimized variant, K2 Thinking. As someone who works with large language models, I wanted to break down what makes this release interesting and where it pushes forward the state of open-source AI. K2 Thinking is a 1-trillion parameter model that can

FastLongSpeech: 30x Compression That Doesn't Murder Your Context

FastLongSpeech: 30x Compression That Doesn't Murder Your Context

Speech models are having a moment and seems like they’re here to stay. They can transcribe your rambling, understand your questions, and even tell when you're being sarcastic. But ask them to process anything longer than a TikTok video and they straight-up collapse. The problem? Speech can

Chain-Talker: Teaching AI to Speak with Empathy

Chain-Talker: Teaching AI to Speak with Empathy

When AI systems engage in conversation, getting the words right is only half the battle. The real challenge is emotional appropriateness: responding to "I just lost my job" with genuine sympathy rather than robotic cheerfulness, or matching enthusiasm when someone shares good news. Current conversational speech synthesis (CSS)

DiTTo-TTS: The TTS System That Doesn't Need Your Phonemes (And Why That's a Big Deal)

DiTTo-TTS: The TTS System That Doesn't Need Your Phonemes (And Why That's a Big Deal)

Text-to-speech has always been that one AI domain where you couldn't just throw data at the problem and call it a day. “Data is the moat” is straight up not a thing here. Want to build a TTS system? Better get comfortable with phonemizers, forced aligners, duration predictors,

What GenSE Gets Right For LLM-Assisted Speech Enhancement 🎙️

What GenSE Gets Right For LLM-Assisted Speech Enhancement 🎙️

Speech enhancement, the art of cleaning up noisy, muffled, or degraded audio, has traditionally been the domain of signal processing algorithms and convolutional networks. But what if we treated speech cleaning like a language problem instead? That's exactly what researchers from Northwestern Polytechnical University and Nanyang Technological University

SafeBench 2025’s top picks: The Benchmarks That Actually Matter for AI Safety

SafeBench 2025’s top picks: The Benchmarks That Actually Matter for AI Safety

You know that feeling when your AI model aces every benchmark but still somehow manages to fail spectacularly in the real world? Yeah, that's exactly why SafeBench exists. While everyone's been obsessing over MMLU scores and coding benchmarks, the real question isn't just "

When AI Snitches: Auditing Agents That Spill Your Model’s (Alignment) Tea

When AI Snitches: Auditing Agents That Spill Your Model’s (Alignment) Tea

Sure, your model aced every benchmark, but can you trust it when the stakes are real? Every frontier lab runs alignment post-training before shipping their chat models to the world. The problem? Actually auditing whether this alignment worked can be an absolute nightmare. You're basically trying to find

A Recipe for Privacy Preserving Autocorrect in GBoard: FL, DP, and Synthetic Data Sprinkles

A Recipe for Privacy Preserving Autocorrect in GBoard: FL, DP, and Synthetic Data Sprinkles

The Personalisation Paradox Training language models for tasks such as autocomplete or error correction isn’t just a matter of fixing typos. Sure, you can turn “pleaes” into “please”, that’s easy. But what about Dave, who always types “frmly” when he means “formally”? You don’t just need autocorrect,

OS-HARM: The AI Safety Benchmark That Puts LLM Agents Through Hell

OS-HARM: The AI Safety Benchmark That Puts LLM Agents Through Hell

Language models have come a long way. From playing autocomplete in your email to writing decent Python scripts, they’ve now levelled up into agents: full-blown task-doers who can click, scroll, type, and wreak havoc across your desktop. These “computer use agents” are smart enough to open your emails, edit