Research
Measured gains from putting memory at inference. Bounded context, lower spend, quality that holds across four domains.
research · production memory
LLMs are stateless. Every call starts cold unless you rebuild context yourself. Full history replay works until sessions get long, then cost grows as O(N²) and attention degrades in the middle of the prompt.
Cortyxia remembers what matters and injects only what this query needs at inference. Context stays bounded, typically 6–12K tokens, turn 1 or turn 50. Quality never takes the hit. Spend does.
80.8%
fewer tokens
governance
10.2×
by question 50
compounding
100%
SWE resolution
vs 73.3% baseline
91.5%
IDE token cut
quality held
What breaks without structured memory
Token inflation. Full-context replay scales linearly per turn and quadratically over a session. In our governance eval, question 50 alone burned 103,972 prompt tokens.
Lost in the middle. The facts are in the prompt, but the model cannot find them. Full-context resolved only 73.3% of SWE-style tasks.
Context silos. Slack, IDE, CRM, and governance tools each keep their own state. Decisions do not travel. Teams re-explain the same policy every session.
cost_shape · session length
What changes when memory sits at inference
You do not need the internals to feel the difference. Here is what production teams get.
Bounded context
Prompts stop growing with every turn. Cortyxia packs only what this query needs into a fixed budget, so spend stays predictable as sessions get longer.
Retrieval temperature
Control how much memory comes back with each call. Focused for everyday answers. Deeper when the work is complex.
Memory that travels
Decisions made in one tool stay available in the next. Teams stop re-explaining the same policy every session, and institutional knowledge stops leaking between apps.
Quality under load
Less noise in the prompt means the model finds the right fact faster. In our evals, efficiency gains came with matched or better answers, not a quality tradeoff.
Retrieval temperature
LLMs have temperature. So should context retrieval.
Models already let you control how creative a response is. Cortyxia lets you control how much memory comes back with each call. Turn it down for a focused answer. Turn it up when the work is complex and the model needs richer context.
Lower setting
Focused answers
Less context in the prompt. Less noise. Best for policy questions, support, and everyday lookups where you want the right fact, not the full archive.
Higher setting
Deeper context
Richer memory for complex work: coding, investigations, multi-step fixes. More structure when the task needs it, without replaying the entire conversation history.
Context size over a coding session
20 turns
Prompt tokens per turn
91.5%
Fewer tokens in a focused coding session versus replaying full history.
~90%
Still cut on the deeper setting, with a bit more context when the task needed it.
100%
Bug-fix success at the highest setting, versus 73% when replaying full history.
Built for production, not demos
Cortyxia sits in the inference path as infrastructure. It remembers across sessions, retrieves what is relevant for the current call, and keeps the prompt lean without asking you to rebuild agents or wire a new integration stack.
proxy or sdk · model-agnostic
Token growth stays bounded
Full-context climbs to ~104K tokens by question 50. Cortyxia stays near ~10K. Early turns carry a small retrieval overhead. By question 6 the advantage flips and never looks back.
Evaluated across four domains
Same architecture. Different workloads. Token savings without trading away answer quality.
Enterprise governance
80.8%
Fewer prompt tokens vs full-context on a 50-question session. Quality held. Advantage compounds to 10.2× by question 50.
Gemini 2.5 Flash
IDE coding
91.5%
Token reduction on a 20-turn emulated IDE session with comparable code quality.
Gemini 3.1 Flash-Lite
SWE-style fixes
70%
Fewer tokens, and Cortyxia resolved 100% of tasks vs 73.3% for full-context.
Gemini 3.1 Flash-Lite
LoCoMo (public)
39.8%
Token reduction on a public long-term conversational memory benchmark with comparable quality.
Gemini 2.5 Flash
Better answers, not just cheaper ones
On SWE-style bug fixing, Cortyxia cut tokens by 70% and still beat full-context on resolution. Structured injection helps the model act instead of drowning in replayed history.
On enterprise governance, Cortyxia posted the highest semantic similarity score versus full-context, sliding window, and summarization, while using 5.2× fewer tokens than full-context on average.
Cortyxia
100%
Full-context baseline
73.3%
Structured context improved both efficiency and task success
Drop-in infrastructure
Cortyxia runs as a transparent proxy between your app and any chat-completions endpoint. One key swap. No rebuild. No MCP project. SDKs in Python and TypeScript are available when you want programmatic control.
Want the full methodology?
We publish measured results and evaluation design. The production system stays proprietary and calibrated per deployment.