Research Paper

Your Base Model Is Smarter Than You Think: And Here's How to Prove It

Your Base Model Is Smarter Than You Think: And Here's How to Prove It

There's a quiet assumption baked into most of the recent excitement around reasoning models: that the impressive gains you see from systems like DeepSeek-R1 or similar RL-trained models come from something genuinely new: novel capabilities that the base model simply didn't have before training. A new

PersonaPlex: Full-Duplex Voice Without the Fixed Persona

PersonaPlex: Full-Duplex Voice Without the Fixed Persona

Introduction Voice AI hit a genuine inflection point when full-duplex models arrived. Systems like Moshi finally cracked the core problem with conversational speech: the awkward cascade of listen, then transcribe, then think, then speak. Full-duplex models [models that listen and speak simultaneously over a continuous audio stream, the same way

Can LLMs Actually Judge Web Development Quality? Spoiler: Not Really

Can LLMs Actually Judge Web Development Quality? Spoiler: Not Really

I recently came across a fascinating paper at ICLR’26 that tackles a question many of us AI developers have been wrestling with: can we trust LLMs to evaluate complex, interactive task? The authors focus on the domain of web development, and the short answer: we've got a

Beyond Autoregression: LLaDA2.1 and the Case for Self-Editing Language Models

Beyond Autoregression: LLaDA2.1 and the Case for Self-Editing Language Models

Introduction Every mainstream large language model today generates text the same way: one token at a time, left to right, no looking back. It works remarkably well, but it has a structural flaw that's easy to overlook until you care about speed at scale. The model can never

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

The Discipline Layer: Harnesses as the Missing Piece in Autonomous Coding

The Discipline Layer: Harnesses as the Missing Piece in Autonomous Coding

Introduction If you've been working with AI agents on longer tasks, you've probably developed your own tricks for dealing with context window limits. Maybe you hit /summarize in Cursor when things get bloated or you ask the agent to write a summary.md file at the

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

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

AgentFold : What If AI Agents Managed Memory Like Humans Do?

AgentFold : What If AI Agents Managed Memory Like Humans Do?

Introduction If you've spent time working with LLM agents for web research, coding assistance in cursor or even extended conversations in ChatGPT, you've probably noticed something: as tasks or multi turn conversations grow longer and more complex, the quality of responses deteriorates - essentially because of

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,

Are Small Language Models the Future of Agentic AI?

Are Small Language Models the Future of Agentic AI?

Recent research from NVIDIA presents a compelling argument that small language models (SLMs) represent the future of agentic artificial intelligence systems. The paper challenges the current industry paradigm of deploying large language models for all agent tasks, proposing instead that smaller, specialized models offer superior operational characteristics for most agentic