Update README with paper information

README.markdown

@@ -9,7 +9,9 @@
 
 CSB (Count and Sample on Bitvectors) is an approximate model counting and almost-uniform sampling tool aimed at solving constraints of bitvectors.
 
-CSB uses [STP](https://github.com/stp/stp) as its frontend and is built on top of that. For counting it uses [ApproxMC](https://github.com/meelgroup/approxmc) (with [Arjun](https://github.com/meelgroup/arjun)). For sampling, it uses [UniSamp](https://www.cs.toronto.edu/~meel/Papers/lics22.pdf).
+To learn more about CSB, please have a look at our [SMT Workshop '24 paper](https://ceur-ws.org/Vol-3725/short2.pdf).
+
+CSB uses [STP](https://github.com/stp/stp) as its frontend and is built on top of that. For counting it uses [ApproxMC](https://github.com/meelgroup/approxmc) (with [Arjun](https://github.com/meelgroup/arjun)). For sampling, it uses [UniGen](https://github.com/meelgroup/unigen/).
 
 ## Build and install
 
@@ -62,42 +64,15 @@ Run with an SMT-LIB2 file:
 ./stp -c myproblem.smt2
 ```
 
-### Python Interface for Almost-uniform Sampler:
-```
-In [1]: import stp
-In [2]: a = stp.Solver()
-In [3]: x = a.bitvec('x')
-In [4]: y = a.bitvec('y')
-In [5]: a.add(x + y < 20)
-In [6]: a.add(x  > 10)
-In [7]: a.add(y  > 10)
-In [8]: a.useUnigen()
-Out[8]: True
-In [9]: a.check()
-Out[9]: True
-In [10]: a.model()
-Out[10]: {'x': 2371135469, 'y': 1923831833}
-```
-
-### Python Interface for Approximate Counter:
-
-```
-In [8]: a.useApproxMC()
-Out[8]: True
-In [9]: a.count()
-Out[9]: 180388626432
-```
-
 ## Architecture
 
-**CSB** converts bitvector constraints into SAT using [STP](https://github.com/stp/stp), integrating with [ApproxMC](https://github.com/meelgroup/approxmc) or [UniSamp](https://github.com/arijitsh/unigen/tree/unisamp) based on specific needs of counting or sampling. This tool is developed from a study on model counters and bitblasting tools, as detailed in [this study](https://arijitsh.github.io/papers/sharpsmt.pdf).
-
+**CSB** converts bitvector constraints into SAT using [STP](https://github.com/stp/stp), integrating with [ApproxMC](https://github.com/meelgroup/approxmc) or [UniGen](https://github.com/meelgroup/unigen/) based on specific needs of counting or sampling. The benchmarks used for evaluating CSB in the SMT workshop paper are available [here](https://utoronto-my.sharepoint.com/:u:/g/personal/arijit_shaw_mail_utoronto_ca/EWcTcfGobH5Jl5SwjzRu6TQB169vWwTnjg-IXWiHJwmuDA?e=MFuxUM).
 
 
 # Authors
 
 * [Arijit Shaw](https://arijitsh.github.io)
 * [Kuldeep S. Meel](https://www.cs.toronto.edu/~meel/)
 
-Please refer to  STP/UniSamp/ApproxMC for the respective authors.
+Please refer to  STP/UniGen/ApproxMC for the respective authors.